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no-repeat 6px;background-position:center;margin-top:10px;min-width:55px;display:none;} @media (min-width:768px){.gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption .expand_btn{display:none;}} .gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-text{-webkit-flex:9;-ms-flex:9;flex:9;text-align:left;padding:21px 37px 10px 0px;font-size:16px;font-weight:700;} @media (min-width:768px){.gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-text{padding:19px 18px 10px 18px;}} @media (min-width:992px){.gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-text{padding:19px 21px 10px 21px;}} .gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption 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no-repeat 6px;background-position:center;padding:16px 0 16px 0;min-width:62px;max-width:62px;} @media (min-width:768px){.gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-partial-url{padding:16px 0 17px 0;min-width:52px;max-width:52px;}} @media (min-width:992px){.gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-partial-url{padding:15px 0 17px 0;min-width:60px;max-width:60px;}} .gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption .caption-partial-url:hover{-webkit-text-decoration:none;text-decoration:none;} .gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption-text{font-size:14px;padding:0px 18px 17px 22px;} @media (min-width:768px){.gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption-text{padding:0px 15px 15px 18px;}} @media (min-width:992px){.gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption-text{padding:0px 18px 19px 21px;}} .gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption-text figcaption{margin:0;padding:0;} .gPLMVn .floats-partial-section .floats-partial-footer .floats-partial-caption-text svg{max-width:100%;} .gPLMVn ol{font-size:16px;padding-left:32px;font-family:STIXGeneral-Regular;line-height:24px;margin:15px 0;} .gPLMVn ol li{margin:10px 0;padding-left:10px;word-break:normal;} .gPLMVn ol li a{word-break:break-all;} .gPLMVn ol li .reflinks{display:block;} .gPLMVn ol li .reflinks a{color:#4D8A17;-webkit-text-decoration:none;text-decoration:none;cursor:pointer;} .gPLMVn ol li .reflinks a:hover{-webkit-text-decoration:underline;text-decoration:underline;} .gPLMVn ol li .reflinks .sep{color:#000000;} .gPLMVn .table-wrapper{width:auto;overflow-x:auto;} .gPLMVn .table-wrapper::-webkit-scrollbar{-webkit-appearance:none;width:5px;height:5px;} .gPLMVn .table-wrapper::-webkit-scrollbar-thumb{border-radius:5px;background-color:rgba(0,0,0,.5);-webkit-box-shadow:0 0 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class="articleContent__ArticleContentsSectionsWrapper-sc-2yl9jy-1 jntdRu article_contents__sections_wrapper"> <div class="articleContent__ArticleWrapper-sc-2yl9jy-2 hAzjHP"> <div class="sc-chbbiW mBGSe article_header_class"> <p class="sc-kZmsYB bkiEfx article_type">研究文章|开放获取</p> <div class="sc-RcBXQ ztvYg article_citation"> 体积 2020年 <span class="sc-fZwumE cUsFHx">|</span>文章的ID 6633252 <span class="sc-fZwumE cUsFHx">|</span> <a class="sc-htpNat bUhGXt link sc-fQejPQ duuwge" href="https://doi.org/10.1155/2020/6633252" aria-label="Doi-link">https://doi.org/10.1155/2020/6633252</a> </div> <div class="sc-LKuAh lcOiDF article_full_citation"> <div class="sc-hzNEM gsmOCv isHide"> <p>振鹏,南苏、谊文秦Jiahuan,大李, ”<span class="adjust-article-svg-size">一个随机森林深处引发网络入侵检测模型</span>”,<i>移动信息系统</我>, 卷。2020年, 文章的ID6633252, 16   页面, 2020年。 https://doi.org/10.1155/2020/6633252</p> </div> <button class="sc-hSdWYo etauus sc-iBEsjs kPshFw" color="">显示引用</button> </div> <h1 class="sc-iRbamj jZOxOO sc-iSDuPN eISAhe article_title adjust-article-svg-size heading-title">一个随机森林深处引发网络入侵检测模型</h1> </div> <div class="sc-jrIrqw eHMFfo article_authors"> <hr class="sc-cEvuZC XkrFe hr__author"> <span class="">振鹏刘<a class="sc-htpNat bUhGXt link" href="https://orcid.org/0000-0002-7466-4622" aria-label="Redirect to Doi" target="_blank"><img alt="" class="sc-EHOje iiusBg sc-iybRtq iQSObO" title="" role="presentation" src="data:image/svg+xml;base64,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" height="16"></a>,<sup>1,2</sup></span> <span class="">南苏<a class="sc-htpNat bUhGXt link" href="https://orcid.org/0000-0002-8971-5314" aria-label="Redirect to Doi" target="_blank"><img alt="" class="sc-EHOje iiusBg sc-iybRtq iQSObO" title="" role="presentation" src="data:image/svg+xml;base64,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" height="16"></a>,<sup>1</sup></span> <span class="">谊文秦<a class="sc-htpNat bUhGXt link" href="https://orcid.org/0000-0003-0913-5019" aria-label="Redirect to Doi" target="_blank"><img alt="" class="sc-EHOje iiusBg sc-iybRtq iQSObO" title="" role="presentation" src="data:image/svg+xml;base64,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" height="16"></a>,<sup>3</sup></span> <span class="">Jiahuan陆<a class="sc-htpNat bUhGXt link" href="https://orcid.org/0000-0003-3311-3004" aria-label="Redirect to Doi" target="_blank"><img alt="" class="sc-EHOje iiusBg sc-iybRtq iQSObO" title="" role="presentation" src="data:image/svg+xml;base64,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" height="16"></a>,<sup>1</sup> 和</span> <span class=""><b>(李</b><a class="sc-htpNat bUhGXt link" href="mailto:" aria-label="Mail Option"><span class="sc-htoDjs kNoiZu sc-hjRWVT irNWJI icon-colored _2zt-1j0LmaF8UjTQAM413G icon-themeable" title="" font-size="16"></span></a><a class="sc-htpNat bUhGXt link" href="https://orcid.org/0000-0003-3472-3461" aria-label="Redirect to Doi" target="_blank"><img alt="" class="sc-EHOje iiusBg sc-iybRtq iQSObO" title="" role="presentation" src="data:image/svg+xml;base64,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" height="16"></a><sup>2</sup></span> <div class="sc-fHxwqH dMuGGA isHide"> <p><sup>1</sup><span>网络安全、计算机学院、河北大学,保定,中国</span></p> <p><sup>2</sup><span>信息技术中心、河北大学、河北保定,中国</span></p> <p><sup>3</sup><span>电气与信息工程学院、兰州交通大学、兰州,中国</span></p> </div> <button class="sc-hSdWYo etauus sc-iQtOjA kfDwZY" color="">显示更多</button> </div> <div class="articleContent__EditorWrapper-sc-2yl9jy-3 evMkvU"> <div class="articleContent__TextWrapper-sc-2yl9jy-8 fYrLtk"> <b>学术编辑器:</b>萨尔瓦多的法令</d我v> <div class="articleContent__PublicationTimeLineWrapper-sc-2yl9jy-4 fjviok"> <div class="articleContent__PublcationCol-sc-2yl9jy-5 hgKieC notPublished"> <span class="articleContent__PublicationField-sc-2yl9jy-6 cIribm">收到了</span> <span class="articleContent__PublicationValue-sc-2yl9jy-7 khHUcu">2020年10月12日</span> </div> <div class="articleContent__PublcationCol-sc-2yl9jy-5 hgKieC notPublished"> <span class="articleContent__PublicationField-sc-2yl9jy-6 cIribm">修改后的</span> <span class="articleContent__PublicationValue-sc-2yl9jy-7 khHUcu">2020年12月06</span> </div> <div class="articleContent__PublcationCol-sc-2yl9jy-5 hgKieC notPublished"> <span class="articleContent__PublicationField-sc-2yl9jy-6 cIribm">接受</span> <span class="articleContent__PublicationValue-sc-2yl9jy-7 khHUcu">2020年12月11日</span> </div> <div class="articleContent__PublcationCol-sc-2yl9jy-5 hgKieC isPublished"> <span class="articleContent__PublicationField-sc-2yl9jy-6 cIribm">发表</span> <span class="articleContent__PublicationValue-sc-2yl9jy-7 khHUcu">2020年12月22日</span> </div> </div> </div> <article class="sc-jtRlXQ gPLMVn article_body "> <div> <div class="xml-content"> <h4 class="header" id="abstract">文摘</h4> <p>本文重点是网络安全的一个重要研究问题。作为一种主动防御技术,入侵检测领域的网络安全中起着重要的作用。传统的入侵检测技术问题,如低精度,检测效率低,耗费时间。机器学习的浅层结构一直未能及时回应。要解决这些问题,基于深度学习方法研究了改善入侵检测。深度学习的优点是有很强的学习能力的特性和能处理非常复杂的数据。因此,我们提出一个深随机与森林有关的网络入侵检测模型。第一阶段使用滑动窗口来段原始特征分成许多小块,然后训练随机森林生成rerepresentation连接类向量。向量将用于训练多级串联并行随机森林在第二阶段。最后,原始数据的分类是由级联的最后一层后投票策略。 Meanwhile, the model is deployed in Spark environment and optimizes cache replacement strategy of RDDs by efficiency sorting and partition integrity check. The experiment results indicate that the proposed method can effectively detect anomaly network behaviors, with high F1-measure scores and high accuracy. The results also show that it can cut down the average execution time on different scaled clusters.</p> <span class="end-abs"></span> <h4 id="introduction">1。介绍</h4> <p>云计算的快速发展,边缘计算,和5 g技术已经广泛渗透到我们的政治、经济、文化和生活的其他方面。每天产生的大量数据从这些场景将促进更有价值从大数据输出;同时,这些广泛应用的前景可以让大数据复杂和不安全。考虑到复杂性、高维度、异质性和处理大量数据的速度,潜在风险不仅存在于系统架构也在数据本身。大多数传统保护解决方案不能满足需求在大数据环境下,因为分布式数据源很难定义数据集的边界,这将威胁到被分析的数据的真实性。</p> <p>离群值也被称为异常或偏差者在数据挖掘和统计分析。在网络空间安全、孤立点检测是一个过程来分析犯罪嫌疑人的键值或行为模式是明显不同于正常的对象。检测算法识别异常,然后清洁确认数据,确保数据安全。异常值检测目前在学术界和业界的一个热门话题。例如,异常斑点出现在磁共振成像或其他类型的医学诊断设备通常表明疾病条件下,从不同寻常的地点和离群值记录在产品支付或频繁的大型交易将有助于发现在金融情况下信用卡欺诈。在社会媒体和其他的例子是谣言检测拥塞检测在城市交通管理(<a href="#B1">1</a>- - - - - -<a href="#B3">3</a>]。在这些挑战中,网络入侵检测是网络安全的关键<a href="#B4">4</a>]。网络入侵检测技术的设计和配置,以确保计算机系统的安全,可以检测计算机网络中违反安全策略的行为。软件和硬件相结合的入侵检测是一个入侵检测系统(IDS)。入侵检测系统(IDS)的目的是分析网络流量或一台机器的活动为了发现nonauthorized活动。这些活动可以是恶意软件或相关的人类攻击(在本地或远程操作<a href="#B5">5</a>]。已经有许多机器学习算法被广泛用于识别异常值在网络<a href="#B6">6</a>- - - - - -<a href="#B8">8</a>]。但最传统的方法主要是有限的和不可接受的精度检测当网络数据往往是复杂和高维的,如反向传播(BP)、支持向量机(SVM)和随机森林(RF) (<a href="#B9">9</a>];的准确性UNSW-NB15数据集不超过91%<a href="#B10">10</a>]。的浅层结构显示,机器学习已经脆弱的回应。尽管入侵检测是一个关键问题,研究方法,结合深入学习和研究机器学习仍不足(<a href="#B11">11</a>]。虽然深层神经网络是强大的,他们是非常复杂和hyperparameters太多,和学习性能严重依赖于这些hyperparameters仔细调优,所以培训成本是巨大的。上面的场景通常使深层神经网络的训练非常努力;有时,它就像一门艺术而不是工程。这启发我们探讨网络异常检测的其他深度学习结构。</p> <p>整体学习是机器学习的一个重要方法,和随机森林是整体学习的经典算法之一。它适合高维数据,只有几个参数,和RF的训练并不复杂。所以,我们考虑使用森林作为一个层来代替我们深的神经元网络结构。除此之外,每一个决策树的训练随机森林是独立的,这是自然的并行部署。每一层的森林深处结构可以并行部署加快培训过程。</p> <p>为了减少造成的障碍众多参数目前的入侵检测和基于深度学习方法进一步提高分类精度和可扩展性,提出了一种基于特征检测模型分割和深层结构的并行随机森林(FS-DPRF)。本文的主要贡献如下。<span class="list"><span class="list-item"><span class="list-label">(1)</span><span class="list-content">提出了一种级联结构的随机森林深处,每一层是并行提高准确性和可伸缩性和适合大规模数据检测任务。可分为各种类型的攻击。</span></span><span class="list-item"><span class="list-label">(2)</span><span class="list-content">介绍了滑动窗口将高维特征分割成小尺寸特征向量训练,这可以减少每个计算的计算量,保持原始信息的完整性。</span></span><span class="list-item"><span class="list-label">(3)</span><span class="list-content">与经典的平行随机森林引发相比,优化替代抽样的方法在内存中加载效率排序和分区完整性检查,可提高集群任务执行效率。</span></span></span></p> <p>验证该模型的性能,与其他算法相比,在四个网络入侵数据集,和实验结果充分证明了该模型在网络异常检测的有效性。</p> <p>本文的其余部分组织如下。部分<a href="#sec2">2</a>评审的相关工作。节<a href="#sec3">3</a>,我们说明用于入侵检测的检测模型。部分<a href="#sec4">4</a>介绍了并行内存优化设计模型。评价模型与一系列的实验部分<a href="#sec5">5</a>。最后,提出了本文的结论部分<a href="#sec6">6</a>。</p> <h4 id="related-work">2。相关工作</h4> <p>目前,许多学者研究了入侵检测问题。最近的一项调查Buczak和Guven<a href="#B12">12</a>)做了一个全面审查当前的入侵检测的数据挖掘和机器学习方法;调查描述算法的优点和缺点,提供了一个明确的未来的工作前景。经典的算法可以分为人工神经网络<a href="#B13">13</a>,<a href="#B14">14</a>),clustering-based,整体学习贝叶斯网络,<a href="#B15">15</a>],基于svm [<a href="#B16">16</a>,隐马尔可夫模型(摘要)。Khalvati et al。<a href="#B17">17</a>)提出了支持向量机的混合学习(距离sum-based SVM, DSSVM)方法。DSSVM,距离总和计算基于每个数据样本之间的相关性和获得的聚类特征维度的数据集,然后使用支持向量机分类及检出率高。Vinayakumar et al。<a href="#B18">18</a>)卷积神经网络(CNN)用于网络入侵检测,研究模型的网络流量时间序列,然后使用监督学习方法模型TCP / IP协议数据包在一个预定义的时间范围。这种网络结构在入侵检测的有效性证明KDD99数据集。Potluri和Diedrich<a href="#B19">19</a>]提出了加速深层神经网络(A-DNN)结构,并用于识别异常网络数据和处理一个加速器的平台。实验结果表明,该方法是可行的和有效的在NSL-KDD。高et al。<a href="#B20">20.</a>]介绍了深层的信念网络异常检测的领域。多层玻耳兹曼机是用来形成一个神经网络分类器。当使用深层信念网络KDD99数据集与支持向量机相比,前者显示了更好的性能。Dominguez et al。<a href="#B21">21</a>)评估无监督算法从不同的研究领域通过大量的对比实验和无监督特征学习工作在大多数情况下,但仍然缺乏可解释性和需要手工分析。Hundman et al。<a href="#B22">22</a>基于LSTM)提出了一个模型和一种新的动态阈值的方法。模型不依赖于稀缺的标签或错误的参数假设处理时间序列数据和实现精度高具有良好的解释能力。Manzoor et al。<a href="#B23">23</a>]介绍了density-based合奏feature-evolving流方法问题,衡量异常值在多尺度或粒度,特别是在高噪声环境下工作。</p> <p>近年来,叠加方法(<a href="#B24">24</a>]在整体学习和提高的方法已经成为流行。刘等人。<a href="#B25">25</a>)提出了隔离森林算法建立一个异常指数的基础上,从叶节点到根节点的路径长度。全球离群值的检测效果很好,但它是弱在当地处理稀疏的点。梯度提高决策树(GBDT)弗里德曼提出的<a href="#B26">26</a>)生成一个预测模型的一组基本的学习者和结合了基本学习者变成一个强大的学习者通过迭代。每次建立模型,模型的损失函数的梯度下降方向将会成立。在连续的迭代中,剩余不断减少产生垂直深化树。它有高的预测精度和鲁棒性强的优点离群值。陈和Guestrin [<a href="#B27">27</a>)提出了一个可伸缩的树增加系统(XGBoost);其主要思想也促进根据损失函数的负梯度方向。最大的区别在于经验误差是由二阶泰勒展开,扩大和一些常规的条目被添加,使损失函数可伸缩、有很高的精度和很好的拟合效果。但是有太多hyperparameters使分类相当依赖于优化结果。苏et al。<a href="#B28">28</a>)提出了一个入侵检测方法使用XGBoost算法在不平衡数据集;它使用改进的击杀算法oversample少数样本和downsample多数样本。方法是基于的前提下,改变原来的特性分布的数据,这不仅增加了模型的计算负担,也容易失去一些重要的信息在样例并影响最终的检测性能。Farnaaz和贾巴<a href="#B8">8</a>)提出了使用随机森林算法来检测各种类型的攻击和NSL-KDD数据验证模型。结果证明DOS的检测精度,探测器,U2R R2L是改善,但功能处理的能力弱。在最新的研究中,罗伯特·et al。<a href="#B5">5</a>)提出了一个probabilistic-driven合奏模型(PDE),使用逻辑回归算法评估整体学习分类器的影响。模型不包括较低的预测概率的分类过程,结合最有效的算法通过加权概率标准。NSL-KDD数据集上的实验表明,PDE的高性能检测入侵。周et al。<a href="#B29">29日</a>)提出了一个新颖的整体系统基于修改的演算法与曲线下的面积(M-AdaBoost-A)算法。诸如SMV的策略和算法应用于多个M-AdaBoost-A建立分类器相结合。它显示了更好的表演两种入侵检测问题:802.11无线网络和传统的企业网络,但它缺乏时间消耗的评估模型。汗等。<a href="#B30">30.</a>)提出了一种深度学习模型(TSDL)基于堆叠autoencoder soft-max分类器。他们的深度学习模式以级联方式工作;模型使用一个概率评分值作为附加的功能最终决定阶段为了检测正常状态和其他类的攻击。TSDL取得了令人印象深刻的成果在UNSW-NB15多级检测的准确性和KDD99。</p> <p>深度学习模型通常具有良好的性能,但它有太多复杂的hyperparameters调整。在大多数情况下,似乎很难有一个好的表现较低复杂性。模型来解决这个问题,我们提出了一个滑动窗口和深层结构在随机森林,提高决策树的多样性,从而提高集成学习的泛化能力和网络入侵检测的准确性,也少得多的参数。与此同时,我们的方法优化了数据缓存替换的抽样火花集群和减少检测任务的执行时间。</p> <h5 id="sec2.1">2.1。算法选择标准</h5> <p>算法的选择需要参阅IDS的体系结构,可以分为集中式结构和分布式结构。</p> <p>大多数IDS算法使用一个单机集中式结构,也就是说,一个主机上执行数据收集和分析。该方法基于主机审计数据进行检测。集中式结构具有结构简单,容易实现。缺点是处理时间是缓慢的。因此,适用于小型网络系统。</p> <p>分布式结构包括层次结构和协作结构。层次结构是树类型分层系统,本文提出的模型,它结合了一个集中的结构的简化和分布式结构的鲁棒性。分布式结构也使得检测时间更快,适合大规模网络系统。</p> <h4 id="model-description">3所示。模型描述</h4> <p>提出了检测模型被描述为功能细分,并行随机森林深处,和投票策略。特征线段模型的第一阶段,这段原始特性来降低高维数据的计算量在一个计算并生成一个连接类向量作为新表示。在第二阶段,连接类向量将用于火车深并行随机森林预测原始数据类型的一个概率分布。最后,投票策略后级联的最后一层将确认异常。图<a href="//www.newsama.com/journals/misy/2020/6633252/fig1/" target="_blank">1</a>显示了FS-DPRF模型的概述。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig1"> <div class="floats-partial-content fig-content-fig1"> <div class="partial-carousel--single-image"> <img alt="" name="6633252.fig.001" src="https://static-01.hindawi.com/articles/misy/volume-2020/6633252/figures/6633252.fig.001.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图1</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/fig1/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> FS-DPRF模型的概述。滑动窗口被创建来降低数据的维数处理。</d我v> </div> </div> <h5 id="sec3.1">3.1。功能段</h5> <p>第一阶段模型中简化了原始数据的特性,如图<a href="//www.newsama.com/journals/misy/2020/6633252/fig2/" target="_blank">2</a>通过使用滑动窗口部分功能分解成许多相同大小的特征向量;每个特征向量的数据维数小于原来的特性,和它减少了计算量在每一个计算随机森林。假设一个线性特征向量的长度<我>n</我>,窗口功能片的长度<我>米,</我>每一次幻灯片1单位长度,<我>n</我>−<我>米</我>+ 1<我>米</我>维特征向量生成。假设有一个包含检测任务<我>c</我>类别、特征处理后的线性特征向量的长度<我>n</我>将生成一个新的特征向量的长度<我>c</我>(<我>n</我>−<我>米</我>+ 1)。类似地,对于一个图像数据,功能段将生成一个新的特征向量的长度<我>c</我>(<我>n</我>−<我>米</我>+ 1)<sup>2</sup>。例如,有一个入侵示例数据包括40特性和有四种类型的攻击如DOS(拒绝服务),R2L(远程到本地),U2R根(用户),和探针(监测和调查)。和片窗口大小设置为10。然后,会有一个共有31个特征向量,其中每个十维。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig2"> <div class="floats-partial-content fig-content-fig2"> <div class="partial-carousel--single-image"> <img alt="" name="6633252.fig.002" src="https://static-01.hindawi.com/articles/misy/volume-2020/6633252/figures/6633252.fig.002.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图2</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/fig2/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 插图的原始特性。最初的功能将被分为许多相同大小的滑动窗口特征向量的维度的降低这部分。</d我v> </div> </div> <p>之后,每个特性向量顺序将放到一个单层随机森林,然后,类概率向量(<a href="#B31">31日</a>将生成。类的生成过程的详细解释概率向量是描绘在图<a href="//www.newsama.com/journals/misy/2020/6633252/fig3/" target="_blank">3</a>。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig3"> <div class="floats-partial-content fig-content-fig3"> <div class="partial-carousel--single-image"> <img alt="" name="6633252.fig.003" src="https://static-01.hindawi.com/articles/misy/volume-2020/6633252/figures/6633252.fig.003.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图3</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/fig3/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 的例子,一个类向量生成一个随机森林。左边的矩形代表输入特征向量的一个实例,和黄色的叶子是每个独立决策树的一个概率分布。</d我v> </div> </div> <p>的熵特征向量计算的基尼系数在节点分裂。基尼指数模型计算熵定义在以下方程。<span class="equation_break" id="EEq1"><span class="left_break" id="L1"></span><span class="middle_break"><span class="center_break" id="M1"> <svg height="37.9605pt" id="M1-1" version="1.1" viewbox="0 0 151.894 37.9605" width="151.894pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B1-1"> <g transform="matrix(.013,0,0,-0.013,0,22.233)"> <path d="M692 302H438V274C537 267 543 260 543 188V103C543 61 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75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z" id="g50-51"></path> </g> <g transform="matrix(.013,0,0,-0.013,148.93,22.233)"> <path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z" id="g113-45"></path> </g> </g> </svg></span></span><span class="right_break" id="R1"> <svg class="label" height="11.44pt" id="M1-2" version="1.1" viewbox="0 0 13.398 11.44" width="13.398pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B1-2"> <g transform="matrix(.013,0,0,-0.013,0,9.26)"> <path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 611 70 451 70 272V271C70 90 136 -71 282 -169L300 -147Z" id="g190-41"></path> </g> <g transform="matrix(.013,0,0,-0.013,4.5,9.26)"> <path d="M311 0V25C221 32 216 41 216 113V635C162 613 103 594 39 583V559L88 556C131 552 135 546 135 500V113C135 41 131 32 40 25V0H311Z" id="g190-213"></path> </g> <g transform="matrix(.013,0,0,-0.013,8.9,9.26)"> <path d="M275 271C275 451 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 269V271Z" id="g190-42"></path> </g> </g> </svg></span></span>在哪里<我>t</我>是目标分割节点,然后呢<svg height="12.7178pt" id="M2" style="vertical-align:-3.42947pt" version="1.1" viewbox="-0.0498162 -9.28833 38.5474 12.7178" width="38.5474pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <use xlink:href="#g113-113"></use> </g> <g transform="matrix(.013,0,0,-0.013,7.71,0)"> <use xlink:href="#g113-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,12.208,0)"> <use xlink:href="#g113-68"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,20.827,3.132)"> <use xlink:href="#g50-108"></use> </g> <g transform="matrix(.013,0,0,-0.013,25.975,0)"> <use xlink:href="#g113-9"></use> </g> <g transform="matrix(.013,0,0,-0.013,29.394,0)"> <use xlink:href="#g113-117"></use> </g> <g transform="matrix(.013,0,0,-0.013,33.827,0)"> <use xlink:href="#g113-42"></use> </g> </svg>代表节点的概率<我>t</我>属于类<我>C</我><sub><i>k</我></sub>。</p> <p>派生类的概率是集团的价值观,最终落在叶子节点,然后平均的预测决策树在森林里,得到输出类向量。31日特征向量之前将转变成31类向量的每一个是四维的。最后,如图<a href="//www.newsama.com/journals/misy/2020/6633252/fig2/" target="_blank">2</a>,所有类向量将连接形成rerepresented特征向量作为增强表示对应于原始数据的功能。和新功能将被用作输入训练随机森林瀑布下一阶段。</p> <h5 id="sec3.2">3.2。并行随机森林深处</h5> <p>并行随机森林层叠堆积的森林深处的结构形式。每个新层的级联结构连接rerepresented特征向量和上一层的类向量作为输入。具体地说,每一层的级联编码脉冲计数输入样本上的所有决策树的预测结果并生成不同的类的概率分布,为类向量。随后,类概率向量转换功能,将连接是由功能分割训练下一层。例如,rerepresented将输入特征向量在第一阶段训练随机森林瀑布。第一层的级联将输出一个四维类向量根据前面的假设;然后,它将与输入特征向量训练下一层等等。并行随机森林深处的结构如图<a href="//www.newsama.com/journals/misy/2020/6633252/fig4/" target="_blank">4</a>。与并行随机森林相比,级联编码脉冲可以提高整体学习的泛化能力。值得注意的是,每一次新的级联层扩展,将随机抽取80%的级联结构的训练集,其余20%的速度增长作为验证集的验证新的级联层的性能。性能改进是低于阈值时,将自动终止训练过程和级联编码脉冲数层将是最终决定。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig4"> <div class="floats-partial-content fig-content-fig4"> <div class="partial-carousel--single-image"> <img alt="" name="6633252.fig.004" src="https://static-01.hindawi.com/articles/misy/volume-2020/6633252/figures/6633252.fig.004.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图4</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/fig4/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 深脉冲重复频率的体系结构,层数,自动确定基于计算性能增益,和训练过程将终止如果改进是低于阈值。</d我v> </div> </div> <h5 id="sec3.3">3.3。投票策略</h5> 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transform="matrix(.013,0,0,-0.013,22.899,0)"> <use xlink:href="#g113-42"></use> </g> </svg>的输出的概率是多少<我>h</我><sub><i>我</我></sub>类标签<我>c</我><sub><i>j</我></sub>。</p> <p>介绍了FS-DPRF算法的详细步骤<a href="//www.newsama.com/journals/misy/2020/6633252/alg1/" target="_blank">1</a>。</p> <div class="floats-partial-section partial-other-wrapper" data-section="alg1"> <div class="floats-partial-content fig-content-alg1"> <table class="algorithm-group"> <tbody> <tr> <td> <table class="algorithm" id="alg1"> <tbody> <tr> <td></td> <td><b>输入</b>:训练数据集<我>D</我>= {(<我>x</我><sub>1</sub>,<我>y</我><sub>1</sub>),(<我>x</我><sub>2</sub>,<我>y</我><sub>2</sub>)…(<我>x</我><sub><i>米</我></sub>,<我>y</我><sub><i>米</我></sub>)};</td> </tr> <tr> <td></td> <td><i>x</我>:潜在的异常数据。</td> </tr> <tr> <td></td> <td><b>输出</b>:<我>H</我>(<我>x</我>):投票结果的样本<我>x</我>;</td> </tr> <tr> <td></td> <td><i>C</我><sub><i>脉冲重复频率</我></sub>:随机森林深处{脉冲重复频率<sub><i>我</我></sub>|<我>我</我>= 1,2,…<我>N</我>}。</td> </tr> <tr> <td>(1)</td> <td><i>C</我><sub><i>脉冲重复频率</我></sub>= {∅}</td> </tr> <tr> <td>(2)</td> <td>初始化hyperparameters:宽容<我>t</我>和窗口大小winSize</td> </tr> <tr> <td>(3)</td> <td><i>D</我>′=功能粒度(<我>D</我>);/ /<我>D</我>′是新生成的特征向量。</td> </tr> <tr> <td>(4)</td> <td><b>做</b></td> </tr> <tr> <td>(5)</td> <td><i>我</我>= 1 / /层<我>我</我>级联编码脉冲。</td> </tr> <tr> <td>(6)</td> <td><b>为</b><i>j</我>= 1,2,…<我>T</我><b>做</b></td> </tr> <tr> <td>(7)</td> <td><i>脉冲重复频率</我><sub><i>我</我></sub>= {∅}</td> </tr> <tr> <td>(8)</td> <td><i>D</我>′<我>j</我>⟵引导抽样(<我>D</我>′)</td> </tr> <tr> <td>(9)</td> <td>树<sub><i>j</我></sub>⟵决策树(<我>D</我>′<sub><i>j</我></sub>)</td> </tr> <tr> <td>(10)</td> <td><i>脉冲重复频率</我><sub><i>我</我></sub>+ ={树<sub><i>j</我></sub>}</td> </tr> <tr> <td>(11)</td> <td><b>结束了</b></td> </tr> <tr> <td>(12)</td> <td><b>如果</b>(公差≥<我>t</我>)</td> </tr> <tr> <td>(13)</td> <td><i>C</我><sub><i>脉冲重复频率</我></sub>+ ={脉冲重复频率<sub><i>我</我></sub>}</td> </tr> <tr> <td>(14)</td> <td><b>其他的</b></td> </tr> <tr> <td>(15)</td> <td>打破</td> </tr> <tr> <td>(16)</td> <td><i>我</我>=<我>我</我>+ 1</td> </tr> <tr> <td>(17)</td> <td><b>而</b>(真正的)</td> </tr> <tr> <td>(18)</td> <td><i>H</我>(<我>x</我>)=投票方法(<我>x</我>)/ /最后一层的选票分类</td> </tr> <tr> <td>(19)</td> <td><b>返回</b>CPRF</td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">算法1。</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/alg1/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> FS-DPRF。</d我v> </div> </div> <h4 id="parallelization-on-spark">4所示。并行化在火花</h4> <p>火花是一个分布式计算框架由加州大学伯克利分校的AMP实验室。火花支持多种方式结合其他大数据平台,使他们能够有效地处理大规模的数据。基于内存的弹性分布式数据集(抽样)机制允许在中间数据缓存在内存中(<a href="#B32">32</a>),节约了大量的I / O操作开销,和很适合迭代和合奏算法。框架处理中具有独特的优势。每个决策树建立射频是彼此独立的,和决策树的每个节点独立也存在分歧。FS-DPRF的结构模型和基于决策树的森林使计算任务有天然的并行性<a href="#B33">33</a>]。然而,训练数据并行随机森林里生成过程需要多个迭代,和大量的抽样数据块需要重用的迭代,直到满足收敛。火花的默认最近最少使用置换算法(LRU)无法应付我们的模型重用的要求抽样数据块,因为它可以很容易地交换high-reuse块缓存,导致低效率的工作执行(<a href="#B34">34</a>]。基于这些事实,缓存层次替换优化为抽样对象,这可以有效地提高集群构建FS-DPRF过程中执行效率。</p> <h5 id="sec4.1">4.1。高重用缓存</h5> <p>首先,火花的缓存机制分配一个缓存管理器每个工人管理抽样和计算缓存大小。抽样数据需要存储大小不大于剩余的内存。否则,更换将会实现。<span class="equation_break" id="EEq4"><span class="left_break" id="L6"></span><span class="middle_break"><span class="center_break" id="M6"> <svg height="66.0641pt" id="M6-1" version="1.1" viewbox="0 0 129.884 66.0641" width="129.884pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B6-1"> <g transform="matrix(.013,0,0,-0.013,0,12.485)"> <path d="M449 634C442 637 425 643 405 650C376 660 341 666 307 666C181 666 98 590 98 485C98 400 170 343 215 310L246 288C307 243 343 204 343 147C343 67 291 18 219 18C104 18 61 124 51 202L23 199C28 124 27 71 27 47C47 22 122 -16 204 -16C324 -16 428 60 428 174C428 256 379 309 307 360L276 382C223 419 179 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</svg></span></span>在哪里<我>年代</我><sub><i>我</我></sub>代表所有抽样的总大小<sub><i>我</我></sub>分区,<我>年代</我><sub><i>ij</我></sub>分区的大小吗<我>j</我>的抽样<sub><i>我</我></sub>。抽样分区之间的计算成本是另一个非常重要的因素,这被定义为<span class="equation_break" id="EEq5"><span class="left_break" id="L7"></span><span class="middle_break"><span class="center_break" id="M7"> <svg height="14.7129pt" id="M7-1" version="1.1" viewbox="0 0 94.874 14.7129" width="94.874pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B7-1"> <g transform="matrix(.013,0,0,-0.013,0,9.184)"> <use xlink:href="#g113-68"></use> </g> <g transform="matrix(.013,0,0,-0.013,8.69,9.184)"> <path d="M620 675H597C578 656 570 650 541 650H144C112 650 104 653 94 675H72C59 618 42 552 23 493L53 491C71 534 88 564 105 585C124 608 144 615 238 615H290L197 121C182 40 174 34 88 28L82 0H361L367 28C275 34 266 38 281 121L374 615H441C522 615 543 608 553 583C562 560 566 531 565 493L597 494C603 551 612 629 620 675Z" id="g113-85"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,15.86,12.316)"> <use xlink:href="#g50-106"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,17.74,12.316)"> <use xlink:href="#g50-107"></use> </g> <g transform="matrix(.013,0,0,-0.013,25.81,9.184)"> <use xlink:href="#g117-34"></use> </g> <g transform="matrix(.013,0,0,-0.013,37.07,9.184)"> <path d="M593 650H142L136 622C219 616 224 611 209 531L130 120C114 39 108 34 23 28L17 0H501C518 32 557 130 568 158L540 169C519 132 495 93 469 70C439 43 409 35 332 35C272 35 241 37 225 50C211 61 211 89 222 149L253 320H350C441 320 443 312 441 239H471L510 434H481C455 366 448 358 360 358H260L303 572C311 613 312 615 348 615H431C509 615 522 603 532 581C544 555 546 531 545 491L574 493C583 555 592 631 593 650Z" id="g113-70"></path> </g> <g transform="matrix(.013,0,0,-0.013,45.08,9.184)"> <use xlink:href="#g113-85"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,52.26,12.316)"> <use xlink:href="#g50-106"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,54.14,12.316)"> <use xlink:href="#g50-107"></use> </g> <g transform="matrix(.013,0,0,-0.013,61.48,9.184)"> <use xlink:href="#g117-33"></use> </g> <g transform="matrix(.013,0,0,-0.013,72.02,9.184)"> <use xlink:href="#g113-84"></use> </g> <g transform="matrix(.013,0,0,-0.013,78.41,9.184)"> <use xlink:href="#g113-85"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,85.59,12.316)"> <use xlink:href="#g50-106"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,87.47,12.316)"> <use xlink:href="#g50-107"></use> </g> <g transform="matrix(.013,0,0,-0.013,91.91,9.184)"> <use xlink:href="#g113-45"></use> </g> </g> </svg></span></span><span class="right_break" id="R7"> <svg class="label" height="11.44pt" id="M7-2" version="1.1" viewbox="0 0 14.898 11.44" width="14.898pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B7-2"> <g transform="matrix(.013,0,0,-0.013,0,9.25)"> <use xlink:href="#g190-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,4.5,9.25)"> <path d="M130 550H367L393 615L386 623H101L63 323C82 331 123 342 165 342C275 342 328 277 328 188C328 107 283 40 201 40C141 40 99 72 78 91C69 99 59 98 51 91C39 81 30 67 29 58C28 48 33 37 46 24C60 10 104 -12 149 -12C203 -11 269 14 323 59C384 109 412 165 412 225C412 308 347 399 219 399C193 399 146 382 104 367L130 550Z" id="g190-161"></path> </g> <g transform="matrix(.013,0,0,-0.013,10.4,9.25)"> <use xlink:href="#g190-42"></use> </g> </g> </svg></span></span>在哪里<我>圣</我><sub><i>ij</我></sub>开始时间,<我>等</我><sub><i>ij</我></sub>是每个抽样分区的结束时间;两者都是通过分区机制引发的依赖。请注意,<我>CT</我><sub><i>ij</我></sub>已经包括了通信开销。因此,我们可以得到每个抽样的重量,定义如下:<span class="equation_break" id="EEq6"><span class="left_break" id="L8"></span><span class="middle_break"><span class="center_break" id="M8"> <svg height="85.3946pt" id="M8-1" version="1.1" viewbox="0 0 144.654 85.3946" width="144.654pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B8-1"> <g transform="matrix(.013,0,0,-0.013,0,23.877)"> <path d="M962 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<p>在这里,<我>W</我>(<我>R</我><sub><i>ij</我></sub>)是分区的重量<我>j</我>的抽样<sub><i>我</我></sub><i>,</我>和<我>W</我>(<我>R</我><sub><i>我</我></sub>)是抽样的重量<sub><i>我</我></sub>。<我>μ</我>是一个影响因子定义为一个不同的工作环境。<我>f</我>(<我>R</我><sub><i>ij</我></sub>)代表分区的使用次数<我>j</我>的抽样<sub><i>我</我></sub>。</p> <p>第二,处理时间是线性相关的数据块的大小。的执行时间抽样可以用百分比抽样大小占据内存大小的火花集群环境。<span class="equation_break" id="EEq7"><span class="left_break" id="L9"></span><span class="middle_break"><span class="center_break" id="M9"> <svg height="55.6618pt" id="M9-1" version="1.1" viewbox="0 0 83.734 55.6618" width="83.734pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B9-1"> <g transform="matrix(.013,0,0,-0.013,0,19.726)"> <use xlink:href="#g113-85"></use> </g> <g transform="matrix(.013,0,0,-0.013,10.47,19.726)"> <use xlink:href="#g119-133"></use> </g> <g transform="matrix(.013,0,0,-0.013,16.25,19.726)"> <use xlink:href="#g113-83"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,24.33,22.858)"> <use 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</g> </g> </svg> <svg class="alt_label" height="79.0731pt" id="IL9" version="1.1" viewbox="0 0 15.23 79.0731" width="15.23pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <use transform="matrix(1 0 0 1 0 10.18)" xlink:href="#B9-2"></use> <use transform="matrix(1 0 0 1 0 54.15)" xlink:href="#B9-6"></use> </svg></span></span>在哪里<我>T</我>(<我>R</我><sub><i>ij</我></sub>)代表分区的执行时间<我>R</我><sub><i>ij</我></sub>。<我>年代</我><sub><i>ij</我></sub>的分区大小<我>R</我><sub><i>ij</我></sub>,<我>年代</我><sub>缓存</sub>集群的记忆中。因为每个分区并行执行任务集下的抽样,抽样的总执行时间是所有分区中最长的。最后,执行效率的抽样可以量化为体重的比例随机数字拨号改为执行时间的价值,和<我>ε</我>(<我>R</我><sub><i>我</我></sub>)是用来表示每个抽样的执行效率,这是定义在以下方程:<span class="equation_break" id="EEq9"><span class="left_break" id="L10"></span><span class="middle_break"><span class="center_break" id="M10"> <svg height="33.5305pt" id="M10-1" version="1.1" viewbox="0 0 85.084 33.5305" width="85.084pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B10-1"> <g 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xlink:href="#g113-47"></use> </g> </g> </svg></span></span><span class="right_break" id="R10"> <svg class="label" height="11.44pt" id="M10-2" version="1.1" viewbox="0 0 15.338 11.44" width="15.338pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B10-2"> <g transform="matrix(.013,0,0,-0.013,0,9.25)"> <use xlink:href="#g190-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,4.5,9.25)"> <path d="M247 635C117 635 41 518 41 422C41 317 114 239 220 239C239 239 256 244 278 255L348 290C315 139 207 37 63 13L67 -15C93 -15 155 -5 207 17C343 71 443 203 443 385C443 520 371 635 247 635ZM231 602C331 602 355 476 355 390C355 371 354 349 351 325C334 310 298 294 261 294C177 294 128 369 128 457C128 516 155 602 231 602Z" id="g190-193"></path> </g> <g transform="matrix(.013,0,0,-0.013,10.84,9.25)"> <use xlink:href="#g190-42"></use> </g> </g> </svg></span></span></p> <p>有向无环图(DAG)的火花将生成抽样的抽样阶段,结构树;然后计算每个抽样和缓存的执行效率高重用抽样的地图<sub>cacheList</sub>抽样<sub><i>我</我></sub>,<我>ε</我>)。高重用缓存方法的步骤描述算法<a href="//www.newsama.com/journals/misy/2020/6633252/alg2/" target="_blank">2</a>。</p> <div class="floats-partial-section partial-other-wrapper" data-section="alg2"> <div class="floats-partial-content fig-content-alg2"> <table class="algorithm-group"> <tbody> <tr> <td> <table class="algorithm" id="alg2"> <tbody> <tr> <td></td> <td><b>输入</b>:<span class="nowrap"> <svg height="12.0262pt" id="M11" style="vertical-align:-3.272861pt" version="1.1" viewbox="-0.0498162 -8.75334 39.6343 12.0262" width="39.6343pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M592 498C586 556 582 629 581 675H560C545 656 537 650 510 650H115C87 650 74 652 60 675H40C38 620 33 555 28 495H57C69 539 77 568 89 585C102 607 122 616 205 616H267V123C267 44 259 34 168 28V0H454V28C360 34 352 44 352 123V616H422C498 616 514 608 531 584C542 568 552 543 562 495L592 498Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,6.938,0)"> <path d="M181 342V451C133 431 89 419 40 411V388C98 381 102 377 102 311V104C102 38 95 32 33 26V0H263V26C186 32 181 38 181 104V287C203 343 235 372 261 372C277 372 289 366 304 352C310 346 318 345 330 350C349 359 362 379 362 399C362 422 338 449 304 449C256 449 213 393 183 342H181Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,11.591,0)"> <path d="M380 106C343 72 306 56 265 56C195 56 116 112 115 248C235 252 361 262 377 265C396 269 400 277 400 297C400 374 333 449 250 449H249C198 449 144 421 103 376S37 269 37 201C37 88 109 -12 232 -12C263 -12 332 6 395 84L380 106ZM225 412C281 412 315 364 314 312C314 297 308 292 290 292C232 290 176 289 120 289C135 370 180 412 225 412Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,17.182,0)"> <path d="M380 106C343 72 306 56 265 56C195 56 116 112 115 248C235 252 361 262 377 265C396 269 400 277 400 297C400 374 333 449 250 449H249C198 449 144 421 103 376S37 269 37 201C37 88 109 -12 232 -12C263 -12 332 6 395 84L380 106ZM225 412C281 412 315 364 314 312C314 297 308 292 290 292C232 290 176 289 120 289C135 370 180 412 225 412Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,22.796,3.132)"> <path d="M181 342V451C133 431 89 419 40 411V388C98 381 102 377 102 311V104C102 38 95 32 33 26V0H263V26C186 32 181 38 181 104V287C203 343 235 372 261 372C277 372 289 366 304 352C310 346 318 345 330 350C349 359 362 379 362 399C362 422 338 449 304 449C256 449 213 393 183 342H181Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,26.054,3.132)"> <path d="M517 51L485 54C448 58 441 63 441 115V712C404 700 337 684 285 678V653C357 648 362 645 362 580V437C339 446 309 449 295 449C159 449 38 340 38 201C38 61 143 -12 223 -12C234 -12 261 -6 301 17L362 53V-12C420 9 495 22 517 26V51ZM362 85C338 67 301 51 266 51C201 51 128 109 128 228C128 373 212 411 259 411C296 411 338 395 362 360V85Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,30.858,3.132)"> <path d="M517 51L485 54C448 58 441 63 441 115V712C404 700 337 684 285 678V653C357 648 362 645 362 580V437C339 446 309 449 295 449C159 449 38 340 38 201C38 61 143 -12 223 -12C234 -12 261 -6 301 17L362 53V-12C420 9 495 22 517 26V51ZM362 85C338 67 301 51 266 51C201 51 128 109 128 228C128 373 212 411 259 411C296 411 338 395 362 360V85Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,35.636,3.132)"> <path d="M319 325C317 349 306 409 297 431C277 440 250 449 209 449C117 449 57 389 57 319C57 243 122 209 182 182C232 159 261 135 261 91C261 48 227 21 190 21C130 21 85 79 68 145L41 140C41 104 51 36 58 22C75 7 121 -12 172 -12C252 -12 337 35 337 126C337 195 286 231 210 262C166 281 126 304 126 348C126 388 152 417 191 417C240 417 274 378 294 318L319 325Z"></path> </g> </svg>:</span>抽样结构。</td> </tr> <tr> <td></td> <td><b>输出</b>:<span class="nowrap"> <svg height="13.0518pt" id="M12" style="vertical-align:-3.743899pt" version="1.1" viewbox="-0.0498162 -9.3079 99.2757 13.0518" width="99.2757pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M861 0V28C774 35 771 41 768 147L759 509C756 612 762 614 851 622V650H681L449 149L221 650H57V622C148 613 153 609 144 479L130 271C123 166 117 123 111 88C104 46 85 34 26 28V0H259V28C192 35 169 42 167 90C166 130 166 173 170 256L185 541H187L411 7H431L675 555H679L683 147C683 41 680 35 598 28V0H861Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,11.361,0)"> <path d="M433 39L423 65C413 59 399 54 387 54C370 54 352 69 352 114V299C352 352 342 392 307 422C285 440 255 449 225 449C168 437 102 399 75 379C56 365 44 353 44 339C44 315 69 296 87 296C101 296 111 303 116 319C124 349 133 371 145 385C156 397 171 404 190 404C241 404 275 364 275 291V274C253 256 180 229 120 209C65 190 39 159 39 110C39 47 88 -12 159 -12C189 -12 237 25 277 52C282 35 288 21 301 8C312 -3 333 -12 348 -12L433 39ZM275 84C256 65 221 48 195 48C164 48 124 73 124 124C124 161 146 180 185 198C206 208 254 229 275 240V84Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,16.82,0)"> <path d="M169 380V459C122 440 66 423 24 416V392C86 384 90 382 90 317V-135C90 -201 81 -207 17 -213V-240H253V-213C176 -207 169 -201 169 -125V6C182 -1 208 -11 238 -12C368 12 487 109 487 260C487 358 421 449 310 449C298 449 279 444 261 433L169 380ZM169 346C196 367 237 389 269 389C341 389 403 329 403 221C403 109 347 37 263 37C228 37 191 53 169 76V346Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,23.723,3.603)"> <path d="M390 111C344 68 312 56 269 56C212 56 118 102 118 241C118 346 175 401 241 401C277 401 312 388 342 360C350 352 355 349 361 349C372 349 394 371 394 392C394 403 391 411 378 422C362 436 329 449 288 449H287C250 449 190 432 138 392C71 341 37 274 37 197C37 90 112 -12 238 -12C297 -12 363 32 407 90L390 111Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,27.599,3.603)"> <path d="M433 39L423 65C413 59 399 54 387 54C370 54 352 69 352 114V299C352 352 342 392 307 422C285 440 255 449 225 449C168 437 102 399 75 379C56 365 44 353 44 339C44 315 69 296 87 296C101 296 111 303 116 319C124 349 133 371 145 385C156 397 171 404 190 404C241 404 275 364 275 291V274C253 256 180 229 120 209C65 190 39 159 39 110C39 47 88 -12 159 -12C189 -12 237 25 277 52C282 35 288 21 301 8C312 -3 333 -12 348 -12L433 39ZM275 84C256 65 221 48 195 48C164 48 124 73 124 124C124 161 146 180 185 198C206 208 254 229 275 240V84Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,31.594,3.603)"> <path d="M390 111C344 68 312 56 269 56C212 56 118 102 118 241C118 346 175 401 241 401C277 401 312 388 342 360C350 352 355 349 361 349C372 349 394 371 394 392C394 403 391 411 378 422C362 436 329 449 288 449H287C250 449 190 432 138 392C71 341 37 274 37 197C37 90 112 -12 238 -12C297 -12 363 32 407 90L390 111Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,35.389,3.603)"> <path d="M513 0V26C455 31 447 37 447 103V278C447 398 390 449 309 449C255 449 202 412 166 376V712C127 700 67 684 19 677V653C85 647 87 643 87 580V103C87 37 79 31 19 26L18 0H231V26C171 32 166 39 166 103V341C194 373 232 392 270 392C337 392 368 351 368 269V103C368 38 360 32 302 26V0H513Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,40.212,3.603)"> <path d="M380 106C343 72 306 56 265 56C195 56 116 112 115 248C235 252 361 262 377 265C396 269 400 277 400 297C400 374 333 449 250 449H249C198 449 144 421 103 376S37 269 37 201C37 88 109 -12 232 -12C263 -12 332 6 395 84L380 106ZM225 412C281 412 315 364 314 312C314 297 308 292 290 292C232 290 176 289 120 289C135 370 180 412 225 412Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,44.079,3.603)"> <path d="M495 163C480 117 462 85 444 65C421 39 387 34 332 34C290 34 256 36 236 47C218 57 213 77 213 131V526C213 612 222 616 301 622V650H40V622C122 616 128 611 128 526V126C128 41 120 34 36 28V0H489C498 31 519 126 525 157L495 163Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,49.03,3.603)"> <path d="M135 536C164 536 186 560 186 587C186 617 164 639 136 639C109 639 85 617 85 587C85 560 109 536 135 536ZM252 0V26C188 32 181 38 181 106V451C138 433 90 420 39 412V388C99 379 102 374 102 312V106C102 38 95 32 32 26V0H252Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,51.441,3.603)"> <path d="M319 325C317 349 306 409 297 431C277 440 250 449 209 449C117 449 57 389 57 319C57 243 122 209 182 182C232 159 261 135 261 91C261 48 227 21 190 21C130 21 85 79 68 145L41 140C41 104 51 36 58 22C75 7 121 -12 172 -12C252 -12 337 35 337 126C337 195 286 231 210 262C166 281 126 304 126 348C126 388 152 417 191 417C240 417 274 378 294 318L319 325Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,54.735,3.603)"> <path d="M298 36L289 62C276 55 253 45 228 45C202 45 169 60 169 141V397H276C289 405 292 426 282 437H169V574L155 576L90 509V437H45L17 408L21 397H90V107C90 28 125 -12 188 -12C198 -12 213 -8 230 1L298 36Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,58.136,0)"> <path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,62.634,0)"> <path d="M181 342V451C133 431 89 419 40 411V388C98 381 102 377 102 311V104C102 38 95 32 33 26V0H263V26C186 32 181 38 181 104V287C203 343 235 372 261 372C277 372 289 366 304 352C310 346 318 345 330 350C349 359 362 379 362 399C362 422 338 449 304 449C256 449 213 393 183 342H181Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,67.287,0)"> <path d="M517 51L485 54C448 58 441 63 441 115V712C404 700 337 684 285 678V653C357 648 362 645 362 580V437C339 446 309 449 295 449C159 449 38 340 38 201C38 61 143 -12 223 -12C234 -12 261 -6 301 17L362 53V-12C420 9 495 22 517 26V51ZM362 85C338 67 301 51 266 51C201 51 128 109 128 228C128 373 212 411 259 411C296 411 338 395 362 360V85Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,74.151,0)"> <path d="M517 51L485 54C448 58 441 63 441 115V712C404 700 337 684 285 678V653C357 648 362 645 362 580V437C339 446 309 449 295 449C159 449 38 340 38 201C38 61 143 -12 223 -12C234 -12 261 -6 301 17L362 53V-12C420 9 495 22 517 26V51ZM362 85C338 67 301 51 266 51C201 51 128 109 128 228C128 373 212 411 259 411C296 411 338 395 362 360V85Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,81.084,3.132)"> <path d="M250 606C250 634 233 656 203 656C168 656 146 618 146 593C146 564 169 545 192 545C227 545 250 573 250 606ZM227 95L212 119C187 98 152 71 135 71C129 71 128 78 134 102L207 373C219 418 217 451 194 451C165 451 92 411 30 351L44 326C77 353 106 371 114 371C124 371 121 357 117 341L55 97C32 5 46 -12 70 -12C108 -12 191 51 227 95Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,84.14,0)"> <path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z"></path> </g>  <g transform="matrix(.013,0,0,-0.013,89.283,0)"> <path d="M387 375C387 402 357 448 257 448C172 448 82 404 82 326C82 289 108 255 156 241V239C85 223 23 181 23 116C23 39 89 -12 182 -12C265 -12 336 31 378 91L361 114C320 73 269 47 216 47C157 47 115 82 115 137C115 191 160 219 218 219C243 219 262 218 272 217L304 259L302 266C295 265 281 264 255 264C195 264 163 294 163 335C163 377 200 416 249 416C293 416 321 389 329 342C331 332 335 329 341 329C355 329 387 352 387 375Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,94.614,0)"> <path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"></path> </g> </svg>:</span>缓存抽样的集合。</td> </tr> <tr> <td>(1)</td> <td><b>为</b>(<我>我</我>= 0<span class="nowrap"> <svg height="12.0262pt" id="M13" style="vertical-align:-3.272861pt" version="1.1" viewbox="-0.0498162 -8.75334 39.6343 12.0262" width="39.6343pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M592 498C586 556 582 629 581 675H560C545 656 537 650 510 650H115C87 650 74 652 60 675H40C38 620 33 555 28 495H57C69 539 77 568 89 585C102 607 122 616 205 616H267V123C267 44 259 34 168 28V0H454V28C360 34 352 44 352 123V616H422C498 616 514 608 531 584C542 568 552 543 562 495L592 498Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,6.938,0)"> <path d="M181 342V451C133 431 89 419 40 411V388C98 381 102 377 102 311V104C102 38 95 32 33 26V0H263V26C186 32 181 38 181 104V287C203 343 235 372 261 372C277 372 289 366 304 352C310 346 318 345 330 350C349 359 362 379 362 399C362 422 338 449 304 449C256 449 213 393 183 342H181Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,11.591,0)"> <path d="M380 106C343 72 306 56 265 56C195 56 116 112 115 248C235 252 361 262 377 265C396 269 400 277 400 297C400 374 333 449 250 449H249C198 449 144 421 103 376S37 269 37 201C37 88 109 -12 232 -12C263 -12 332 6 395 84L380 106ZM225 412C281 412 315 364 314 312C314 297 308 292 290 292C232 290 176 289 120 289C135 370 180 412 225 412Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,17.182,0)"> <path d="M380 106C343 72 306 56 265 56C195 56 116 112 115 248C235 252 361 262 377 265C396 269 400 277 400 297C400 374 333 449 250 449H249C198 449 144 421 103 376S37 269 37 201C37 88 109 -12 232 -12C263 -12 332 6 395 84L380 106ZM225 412C281 412 315 364 314 312C314 297 308 292 290 292C232 290 176 289 120 289C135 370 180 412 225 412Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,22.796,3.132)"> <path d="M181 342V451C133 431 89 419 40 411V388C98 381 102 377 102 311V104C102 38 95 32 33 26V0H263V26C186 32 181 38 181 104V287C203 343 235 372 261 372C277 372 289 366 304 352C310 346 318 345 330 350C349 359 362 379 362 399C362 422 338 449 304 449C256 449 213 393 183 342H181Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,26.054,3.132)"> <path d="M517 51L485 54C448 58 441 63 441 115V712C404 700 337 684 285 678V653C357 648 362 645 362 580V437C339 446 309 449 295 449C159 449 38 340 38 201C38 61 143 -12 223 -12C234 -12 261 -6 301 17L362 53V-12C420 9 495 22 517 26V51ZM362 85C338 67 301 51 266 51C201 51 128 109 128 228C128 373 212 411 259 411C296 411 338 395 362 360V85Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,30.858,3.132)"> <path d="M517 51L485 54C448 58 441 63 441 115V712C404 700 337 684 285 678V653C357 648 362 645 362 580V437C339 446 309 449 295 449C159 449 38 340 38 201C38 61 143 -12 223 -12C234 -12 261 -6 301 17L362 53V-12C420 9 495 22 517 26V51ZM362 85C338 67 301 51 266 51C201 51 128 109 128 228C128 373 212 411 259 411C296 411 338 395 362 360V85Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,35.636,3.132)"> <path d="M319 325C317 349 306 409 297 431C277 440 250 449 209 449C117 449 57 389 57 319C57 243 122 209 182 182C232 159 261 135 261 91C261 48 227 21 190 21C130 21 85 79 68 145L41 140C41 104 51 36 58 22C75 7 121 -12 172 -12C252 -12 337 35 337 126C337 195 286 231 210 262C166 281 126 304 126 348C126 388 152 417 191 417C240 417 274 378 294 318L319 325Z"></path> </g> </svg>。</span>长度是1)</td> </tr> <tr> <td>(2)</td> <td>calc<svg height="12.5794pt" id="M14" style="vertical-align:-3.29107pt" version="1.1" viewbox="-0.0498162 -9.28833 25.6513 12.5794" width="25.6513pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M387 375C387 402 357 448 257 448C172 448 82 404 82 326C82 289 108 255 156 241V239C85 223 23 181 23 116C23 39 89 -12 182 -12C265 -12 336 31 378 91L361 114C320 73 269 47 216 47C157 47 115 82 115 137C115 191 160 219 218 219C243 219 262 218 272 217L304 259L302 266C295 265 281 264 255 264C195 264 163 294 163 335C163 377 200 416 249 416C293 416 321 389 329 342C331 332 335 329 341 329C355 329 387 352 387 375Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,5.331,0)"> <path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,9.829,0)"> <path d="M610 18C585 26 567 34 540 68C517 97 499 128 476 171C452 215 425 276 413 304C496 332 570 394 570 494C570 555 545 595 509 619S419 650 364 650H139L133 622C216 615 219 612 203 527L129 132C112 40 105 36 23 28L17 0H279L285 28C199 34 194 40 211 132L239 284H284C320 284 334 275 351 236C374 182 394 140 420 93C459 23 495 -1 592 -8H600L610 18ZM480 485C480 424 449 372 403 342C374 323 338 316 293 316H245L291 562C296 589 301 601 311 608S337 618 358 618C432 618 480 575 480 485Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,17.915,3.132)"> <path d="M250 606C250 634 233 656 203 656C168 656 146 618 146 593C146 564 169 545 192 545C227 545 250 573 250 606ZM227 95L212 119C187 98 152 71 135 71C129 71 128 78 134 102L207 373C219 418 217 451 194 451C165 451 92 411 30 351L44 326C77 353 106 371 114 371C124 371 121 357 117 341L55 97C32 5 46 -12 70 -12C108 -12 191 51 227 95Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,20.971,0)"> <path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"></path> </g> </svg>/ /计算抽样执行效率</td> </tr> <tr> <td>(3)</td> <td><b>如果</b>(<svg height="12.5794pt" id="M15" style="vertical-align:-3.29107pt" version="1.1" viewbox="-0.0498162 -9.28833 25.6513 12.5794" width="25.6513pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M387 375C387 402 357 448 257 448C172 448 82 404 82 326C82 289 108 255 156 241V239C85 223 23 181 23 116C23 39 89 -12 182 -12C265 -12 336 31 378 91L361 114C320 73 269 47 216 47C157 47 115 82 115 137C115 191 160 219 218 219C243 219 262 218 272 217L304 259L302 266C295 265 281 264 255 264C195 264 163 294 163 335C163 377 200 416 249 416C293 416 321 389 329 342C331 332 335 329 341 329C355 329 387 352 387 375Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,5.331,0)"> <path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,9.829,0)"> <path d="M610 18C585 26 567 34 540 68C517 97 499 128 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-63 45 -147L64 -170C213 -68 275 89 275 270Z"></path> </g> </svg></td> </tr> <tr> <td>(5)</td> <td><b>如果</b></td> </tr> <tr> <td>(6)</td> <td><b>结束了</b></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">算法2。</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/alg2/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 高重用缓存。</d我v> </div> </div> <h5 id="sec4.2">4.2。分层的替换</h5> <p>分层更换是第二步的并行优化。它分类抽样的目标在更换之前,优先处理不完整的抽样。因为它是图所示<a href="//www.newsama.com/journals/misy/2020/6633252/fig5/" target="_blank">5</a>,我们设计IntegrityCheck验证抽样函数,函数将检查分区和记下标记的完整集合中的记录分区状态。如果抽样的分区是不完整的,它将被标记为错误和被取代;否则,它将被标记为真实的。然后,抽样效率较低根据地图将被替换<sub>cacheList</sub>抽样<sub><i>我</我></sub>,<我>ε</我>)。分层的过程替代提出了算法<a href="//www.newsama.com/journals/misy/2020/6633252/alg3/" target="_blank">3</a>。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig5"> <div class="floats-partial-content fig-content-fig5"> <div class="partial-carousel--single-image"> <img alt="" name="6633252.fig.005" src="https://static-01.hindawi.com/articles/misy/volume-2020/6633252/figures/6633252.fig.005.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图5</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/fig5/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 详细的工作原理,完整性检查功能。</d我v> </div> </div> <div class="floats-partial-section partial-other-wrapper" data-section="alg3"> <div class="floats-partial-content fig-content-alg3"> <table class="algorithm-group"> <tbody> <tr> <td> <table class="algorithm" id="alg3"> <tbody> <tr> <td></td> <td><b>输入</b>:<span class="nowrap"> <svg height="13.0322pt" id="M17" 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451C165 451 92 411 30 351L44 326C77 353 106 371 114 371C124 371 121 357 117 341L55 97C32 5 46 -12 70 -12C108 -12 191 51 227 95Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,85.543,0)"> <path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z"></path> </g>  <g transform="matrix(.013,0,0,-0.013,90.686,0)"> <path d="M387 375C387 402 357 448 257 448C172 448 82 404 82 326C82 289 108 255 156 241V239C85 223 23 181 23 116C23 39 89 -12 182 -12C265 -12 336 31 378 91L361 114C320 73 269 47 216 47C157 47 115 82 115 137C115 191 160 219 218 219C243 219 262 218 272 217L304 259L302 266C295 265 281 264 255 264C195 264 163 294 163 335C163 377 200 416 249 416C293 416 321 389 329 342C331 332 335 329 341 329C355 329 387 352 387 375Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,96.017,0)"> <path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"></path> </g> </svg>:</span>缓存的抽样,<span class="nowrap"> <svg height="11.9087pt" id="M18" style="vertical-align:-3.2728pt" version="1.1" viewbox="-0.0498162 -8.6359 27.1245 11.9087" width="27.1245pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M449 634C442 637 425 643 405 650C376 660 341 666 307 666C181 666 98 590 98 485C98 400 170 343 215 310L246 288C307 243 343 204 343 147C343 67 291 18 219 18C104 18 61 124 51 202L23 199C28 124 27 71 27 47C47 22 122 -16 204 -16C324 -16 428 60 428 174C428 256 379 309 307 360L276 382C223 419 179 455 179 516C179 576 221 632 293 632C379 632 410 564 418 487L448 490C446 536 446 592 449 634Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,6.071,3.132)"> <path d="M390 111C344 68 312 56 269 56C212 56 118 102 118 241C118 346 175 401 241 401C277 401 312 388 342 360C350 352 355 349 361 349C372 349 394 371 394 392C394 403 391 411 378 422C362 436 329 449 288 449H287C250 449 190 432 138 392C71 341 37 274 37 197C37 90 112 -12 238 -12C297 -12 363 32 407 90L390 111Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,9.948,3.132)"> <path d="M433 39L423 65C413 59 399 54 387 54C370 54 352 69 352 114V299C352 352 342 392 307 422C285 440 255 449 225 449C168 437 102 399 75 379C56 365 44 353 44 339C44 315 69 296 87 296C101 296 111 303 116 319C124 349 133 371 145 385C156 397 171 404 190 404C241 404 275 364 275 291V274C253 256 180 229 120 209C65 190 39 159 39 110C39 47 88 -12 159 -12C189 -12 237 25 277 52C282 35 288 21 301 8C312 -3 333 -12 348 -12L433 39ZM275 84C256 65 221 48 195 48C164 48 124 73 124 124C124 161 146 180 185 198C206 208 254 229 275 240V84Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,13.943,3.132)"> <path d="M390 111C344 68 312 56 269 56C212 56 118 102 118 241C118 346 175 401 241 401C277 401 312 388 342 360C350 352 355 349 361 349C372 349 394 371 394 392C394 403 391 411 378 422C362 436 329 449 288 449H287C250 449 190 432 138 392C71 341 37 274 37 197C37 90 112 -12 238 -12C297 -12 363 32 407 90L390 111Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,17.737,3.132)"> <path d="M513 0V26C455 31 447 37 447 103V278C447 398 390 449 309 449C255 449 202 412 166 376V712C127 700 67 684 19 677V653C85 647 87 643 87 580V103C87 37 79 31 19 26L18 0H231V26C171 32 166 39 166 103V341C194 373 232 392 270 392C337 392 368 351 368 269V103C368 38 360 32 302 26V0H513Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,22.56,3.132)"> <path d="M380 106C343 72 306 56 265 56C195 56 116 112 115 248C235 252 361 262 377 265C396 269 400 277 400 297C400 374 333 449 250 449H249C198 449 144 421 103 376S37 269 37 201C37 88 109 -12 232 -12C263 -12 332 6 395 84L380 106ZM225 412C281 412 315 364 314 312C314 297 308 292 290 292C232 290 176 289 120 289C135 370 180 412 225 412Z"></path> </g> </svg>:</span>集群缓存大小,<span class="nowrap"> <svg height="11.8174pt" id="M19" style="vertical-align:-3.1815pt" version="1.1" viewbox="-0.0498162 -8.6359 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18 677V653C81 647 87 645 87 577V104C87 38 78 32 15 26V0H238Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,14.734,3.132)"> <path d="M238 0V26C174 32 166 38 166 104V712C132 700 70 683 18 677V653C81 647 87 645 87 577V104C87 38 78 32 15 26V0H238Z"></path> </g> </svg>:</span>缓存的抽样大小,<span class="nowrap"> <svg height="11.9087pt" id="M20" style="vertical-align:-3.2728pt" version="1.1" viewbox="-0.0498162 -8.6359 23.8858 11.9087" width="23.8858pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M610 18C585 26 567 34 540 68C517 97 499 128 476 171C452 215 425 276 413 304C496 332 570 394 570 494C570 555 545 595 509 619S419 650 364 650H139L133 622C216 615 219 612 203 527L129 132C112 40 105 36 23 28L17 0H279L285 28C199 34 194 40 211 132L239 284H284C320 284 334 275 351 236C374 182 394 140 420 93C459 23 495 -1 592 -8H600L610 18ZM480 485C480 424 449 372 403 342C374 323 338 316 293 316H245L291 562C296 589 301 601 311 608S337 618 358 618C432 618 480 575 480 485Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,8.086,3.132)"> <path d="M524 0V26C466 32 460 36 460 104V297C460 393 411 449 331 449C302 449 276 437 248 419C223 402 201 387 181 372V451C137 432 90 420 42 411V388C96 378 102 374 102 310V104C102 38 97 33 29 26V0H246V26C187 32 181 36 181 104V339C211 365 250 390 290 390C357 390 381 345 381 276V109C381 40 374 32 315 26V0H524Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,13.027,3.132)"> <path d="M380 106C343 72 306 56 265 56C195 56 116 112 115 248C235 252 361 262 377 265C396 269 400 277 400 297C400 374 333 449 250 449H249C198 449 144 421 103 376S37 269 37 201C37 88 109 -12 232 -12C263 -12 332 6 395 84L380 106ZM225 412C281 412 315 364 314 312C314 297 308 292 290 292C232 290 176 289 120 289C135 370 180 412 225 412Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,16.968,3.132)"> <path d="M696 437H516V411C580 402 587 397 574 345C555 271 527 183 505 114H503C461 222 420 328 383 432H357C315 316 278 214 241 118H239C214 195 189 274 165 350C151 396 156 402 216 411V437H9V411C57 405 65 398 83 346C125 226 162 108 199 -11H227C267 100 308 199 346 300C387 195 428 97 468 -11H497C542 124 587 257 617 337C637 392 646 404 696 411V437Z"></path> </g> </svg>:</span>缓存的候选人,<span class="nowrap"> <svg height="11.9087pt" id="M21" style="vertical-align:-3.2728pt" version="1.1" viewbox="-0.0498162 -8.6359 21.8632 11.9087" width="21.8632pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M449 634C442 637 425 643 405 650C376 660 341 666 307 666C181 666 98 590 98 485C98 400 170 343 215 310L246 288C307 243 343 204 343 147C343 67 291 18 219 18C104 18 61 124 51 202L23 199C28 124 27 71 27 47C47 22 122 -16 204 -16C324 -16 428 60 428 174C428 256 379 309 307 360L276 382C223 419 179 455 179 516C179 576 221 632 293 632C379 632 410 564 418 487L448 490C446 536 446 592 449 634Z"></path> </g> <g 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402 216 411V437H9V411C57 405 65 398 83 346C125 226 162 108 199 -11H227C267 100 308 199 346 300C387 195 428 97 468 -11H497C542 124 587 257 617 337C637 392 646 404 696 411V437Z"></path> </g> </svg>:</span>候选人的大小。</td> </tr> <tr> <td></td> <td><b>输出</b>:<svg height="9.5404pt" id="M22" style="vertical-align:-0.2324991pt" version="1.1" viewbox="-0.0498162 -9.3079 72.4119 9.5404" width="72.4119pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M181 342V451C133 431 89 419 40 411V388C98 381 102 377 102 311V104C102 38 95 32 33 26V0H263V26C186 32 181 38 181 104V287C203 343 235 372 261 372C277 372 289 366 304 352C310 346 318 345 330 350C349 359 362 379 362 399C362 422 338 449 304 449C256 449 213 393 183 342H181Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,4.653,0)"> <path d="M517 51L485 54C448 58 441 63 441 115V712C404 700 337 684 285 678V653C357 648 362 645 362 580V437C339 446 309 449 295 449C159 449 38 340 38 201C38 61 143 -12 223 -12C234 -12 261 -6 301 17L362 53V-12C420 9 495 22 517 26V51ZM362 85C338 67 301 51 266 51C201 51 128 109 128 228C128 373 212 411 259 411C296 411 338 395 362 360V85Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,11.517,0)"> <path d="M517 51L485 54C448 58 441 63 441 115V712C404 700 337 684 285 678V653C357 648 362 645 362 580V437C339 446 309 449 295 449C159 449 38 340 38 201C38 61 143 -12 223 -12C234 -12 261 -6 301 17L362 53V-12C420 9 495 22 517 26V51ZM362 85C338 67 301 51 266 51C201 51 128 109 128 228C128 373 212 411 259 411C296 411 338 395 362 360V85Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,20.557,0)"> <path d="M614 175C564 76 510 21 408 21C256 21 146 149 146 336C146 488 235 629 402 629C510 629 570 586 597 480L626 488C620 541 614 582 606 638C578 643 510 665 429 665C206 665 44 527 44 316C44 157 153 -15 402 -15C474 -15 558 5 586 11C604 45 629 119 643 165L614 175Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,29.319,0)"> <path d="M433 39L423 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392 270 392C337 392 368 351 368 269V103C368 38 360 32 302 26V0H513Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,90.213,3.132)"> <path d="M380 106C343 72 306 56 265 56C195 56 116 112 115 248C235 252 361 262 377 265C396 269 400 277 400 297C400 374 333 449 250 449H249C198 449 144 421 103 376S37 269 37 201C37 88 109 -12 232 -12C263 -12 332 6 395 84L380 106ZM225 412C281 412 315 364 314 312C314 297 308 292 290 292C232 290 176 289 120 289C135 370 180 412 225 412Z"></path> </g> </svg>)</span></td> </tr> <tr> <td>(5)</td> <td><b>为</b>((<我>k</我>,<span class="nowrap"> <svg height="6.04987pt" id="M26" style="vertical-align:-0.2325001pt" version="1.1" viewbox="-0.0498162 -5.81737 5.98461 6.04987" width="5.98461pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M219 86C216 168 211 250 206 337C201 410 189 448 163 448C131 448 79 396 43 344L60 322C91 359 110 375 118 375S132 358 136 298C141 238 152 81 155 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style="vertical-align:-3.743899pt" version="1.1" viewbox="-0.0498162 -9.3079 99.2757 13.0518" width="99.2757pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M861 0V28C774 35 771 41 768 147L759 509C756 612 762 614 851 622V650H681L449 149L221 650H57V622C148 613 153 609 144 479L130 271C123 166 117 123 111 88C104 46 85 34 26 28V0H259V28C192 35 169 42 167 90C166 130 166 173 170 256L185 541H187L411 7H431L675 555H679L683 147C683 41 680 35 598 28V0H861Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,11.361,0)"> <path d="M433 39L423 65C413 59 399 54 387 54C370 54 352 69 352 114V299C352 352 342 392 307 422C285 440 255 449 225 449C168 437 102 399 75 379C56 365 44 353 44 339C44 315 69 296 87 296C101 296 111 303 116 319C124 349 133 371 145 385C156 397 171 404 190 404C241 404 275 364 275 291V274C253 256 180 229 120 209C65 190 39 159 39 110C39 47 88 -12 159 -12C189 -12 237 25 277 52C282 35 288 21 301 8C312 -3 333 -12 348 -12L433 39ZM275 84C256 65 221 48 195 48C164 48 124 73 124 124C124 161 146 180 185 198C206 208 254 229 275 240V84Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,16.82,0)"> <path d="M169 380V459C122 440 66 423 24 416V392C86 384 90 382 90 317V-135C90 -201 81 -207 17 -213V-240H253V-213C176 -207 169 -201 169 -125V6C182 -1 208 -11 238 -12C368 12 487 109 487 260C487 358 421 449 310 449C298 449 279 444 261 433L169 380ZM169 346C196 367 237 389 269 389C341 389 403 329 403 221C403 109 347 37 263 37C228 37 191 53 169 76V346Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,23.723,3.603)"> <path d="M390 111C344 68 312 56 269 56C212 56 118 102 118 241C118 346 175 401 241 401C277 401 312 388 342 360C350 352 355 349 361 349C372 349 394 371 394 392C394 403 391 411 378 422C362 436 329 449 288 449H287C250 449 190 432 138 392C71 341 37 274 37 197C37 90 112 -12 238 -12C297 -12 363 32 407 90L390 111Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,27.599,3.603)"> <path d="M433 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transform="matrix(.013,0,0,-0.013,58.136,0)"> <path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,62.634,0)"> <path d="M181 342V451C133 431 89 419 40 411V388C98 381 102 377 102 311V104C102 38 95 32 33 26V0H263V26C186 32 181 38 181 104V287C203 343 235 372 261 372C277 372 289 366 304 352C310 346 318 345 330 350C349 359 362 379 362 399C362 422 338 449 304 449C256 449 213 393 183 342H181Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,67.287,0)"> <path d="M517 51L485 54C448 58 441 63 441 115V712C404 700 337 684 285 678V653C357 648 362 645 362 580V437C339 446 309 449 295 449C159 449 38 340 38 201C38 61 143 -12 223 -12C234 -12 261 -6 301 17L362 53V-12C420 9 495 22 517 26V51ZM362 85C338 67 301 51 266 51C201 51 128 109 128 228C128 373 212 411 259 411C296 411 338 395 362 360V85Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,74.151,0)"> <path d="M517 51L485 54C448 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375C387 402 357 448 257 448C172 448 82 404 82 326C82 289 108 255 156 241V239C85 223 23 181 23 116C23 39 89 -12 182 -12C265 -12 336 31 378 91L361 114C320 73 269 47 216 47C157 47 115 82 115 137C115 191 160 219 218 219C243 219 262 218 272 217L304 259L302 266C295 265 281 264 255 264C195 264 163 294 163 335C163 377 200 416 249 416C293 416 321 389 329 342C331 332 335 329 341 329C355 329 387 352 387 375Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,94.614,0)"> <path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"></path> </g> </svg>)</span></td> </tr> <tr> <td>(14)</td> <td>v。快速排序();/ /排序的执行效率</td> </tr> <tr> <td>(15)</td> <td><b>如果</b>(<svg height="6.04987pt" id="M35" style="vertical-align:-0.2325001pt" version="1.1" viewbox="-0.0498162 -5.81737 5.98461 6.04987" width="5.98461pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M219 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332 335 329 341 329C355 329 387 352 387 375Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,5.331,0)"> <path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,9.829,0)"> <path d="M610 18C585 26 567 34 540 68C517 97 499 128 476 171C452 215 425 276 413 304C496 332 570 394 570 494C570 555 545 595 509 619S419 650 364 650H139L133 622C216 615 219 612 203 527L129 132C112 40 105 36 23 28L17 0H279L285 28C199 34 194 40 211 132L239 284H284C320 284 334 275 351 236C374 182 394 140 420 93C459 23 495 -1 592 -8H600L610 18ZM480 485C480 424 449 372 403 342C374 323 338 316 293 316H245L291 562C296 589 301 601 311 608S337 618 358 618C432 618 480 575 480 485Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,17.915,3.132)"> <path d="M524 0V26C466 32 460 36 460 104V297C460 393 411 449 331 449C302 449 276 437 248 419C223 402 201 387 181 372V451C137 432 90 420 42 411V388C96 378 102 374 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485Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,8.086,3.132)"> <path d="M524 0V26C466 32 460 36 460 104V297C460 393 411 449 331 449C302 449 276 437 248 419C223 402 201 387 181 372V451C137 432 90 420 42 411V388C96 378 102 374 102 310V104C102 38 97 33 29 26V0H246V26C187 32 181 36 181 104V339C211 365 250 390 290 390C357 390 381 345 381 276V109C381 40 374 32 315 26V0H524Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,13.027,3.132)"> <path d="M380 106C343 72 306 56 265 56C195 56 116 112 115 248C235 252 361 262 377 265C396 269 400 277 400 297C400 374 333 449 250 449H249C198 449 144 421 103 376S37 269 37 201C37 88 109 -12 232 -12C263 -12 332 6 395 84L380 106ZM225 412C281 412 315 364 314 312C314 297 308 292 290 292C232 290 176 289 120 289C135 370 180 412 225 412Z"></path> </g> <g transform="matrix(.0091,0,0,-0.0091,16.968,3.132)"> <path d="M696 437H516V411C580 402 587 397 574 345C555 271 527 183 505 114H503C461 222 420 328 383 432H357C315 316 278 214 241 118H239C214 195 189 274 165 350C151 396 156 402 216 411V437H9V411C57 405 65 398 83 346C125 226 162 108 199 -11H227C267 100 308 199 346 300C387 195 428 97 468 -11H497C542 124 587 257 617 337C637 392 646 404 696 411V437Z"></path> </g> </svg>,</span> <svg height="6.04987pt" id="M38" style="vertical-align:-0.2325001pt" version="1.1" viewbox="-0.0498162 -5.81737 5.98461 6.04987" width="5.98461pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M219 86C216 168 211 250 206 337C201 410 189 448 163 448C131 448 79 396 43 344L60 322C91 359 110 375 118 375S132 358 136 298C141 238 152 81 155 -12H182C242 62 331 177 390 258C435 321 451 360 451 391C450 424 432 448 408 448C390 448 372 435 366 419C362 410 362 401 366 394C373 383 376 367 376 350C376 283 262 138 221 86H219Z"></path> </g> </svg>=<span class="nowrap"> <svg height="12.5612pt" id="M39" style="vertical-align:-3.27287pt" version="1.1" viewbox="-0.0498162 -9.28833 38.2655 12.5612" width="38.2655pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,0)"> <path d="M387 375C387 402 357 448 257 448C172 448 82 404 82 326C82 289 108 255 156 241V239C85 223 23 181 23 116C23 39 89 -12 182 -12C265 -12 336 31 378 91L361 114C320 73 269 47 216 47C157 47 115 82 115 137C115 191 160 219 218 219C243 219 262 218 272 217L304 259L302 266C295 265 281 264 255 264C195 264 163 294 163 335C163 377 200 416 249 416C293 416 321 389 329 342C331 332 335 329 341 329C355 329 387 352 387 375Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,5.331,0)"> <path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path> </g> <g transform="matrix(.013,0,0,-0.013,9.829,0)"> <path d="M610 18C585 26 567 34 540 68C517 97 499 128 476 171C452 215 425 276 413 304C496 332 570 394 570 494C570 555 545 595 509 619S419 650 364 650H139L133 622C216 615 219 612 203 527L129 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transform="matrix(.013,0,0,-0.013,161.606,0)"> <path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"></path> </g> </svg></td> </tr> <tr> <td>(20)</td> <td><b>如果</b></td> </tr> <tr> <td>(21)</td> <td><b>如果</b></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">算法3。</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/alg3/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 分层替换。</d我v> </div> </div> <h4 id="preliminary-assumptions-and-hypotheses">5。初步假设和假设</h4> <p>有三个初步假设性能优良的深度学习模型:<span class="list"><span class="list-item"><span class="list-label">(我)</span><span class="list-content">层一层的处理</span></span><span class="list-item"><span class="list-label">(2)</span><span class="list-content">功能转换</span></span><span class="list-item"><span class="list-label">(3)</span><span class="list-content">足够的模型复杂性</span></span></span></p> <p>传统的机器学习方法如决策树一层一层地处理,但他们缺乏足够的复杂性。合奏的方法可以增加的复杂性,如随机森林,但它仍然是不够复杂,因为没有功能转换过程,和执行的处理总是在同一个特征空间。因此,我们的主要假设是,功能分割和级联结构可以使随机森林增加功能转换能力和足够的复杂性在原来的基础上,从而提高泛化能力。的另一个假设是,优化spark-based抽样缓存替换策略可以降低该模型的训练和检测时间。实验需要角色的转换特性数值特征训练模型。结果和分析下面的证明假说声称。</p> <h4 id="experiment">6。实验</h4> <h5 id="sec6.1">6.1。数据集和预处理</h5> <p>为了评估该模型和实验结果报告,四个选择入侵数据集,即。,NSL-KDD [<a href="#B35">35</a>],UNSW-NB15 [<a href="#B36">36</a>],CICIDS2017, CICIDS2018 [<a href="#B37">37</a>]。</p> <p>NSL-KDD KDD 99数据集的改进是一个收集从一个模拟网络环境美国空军在9周。训练集不包含冗余记录。此外,没有重复的记录在测试集,使检测率更准确。每一块数据包含43特性包括一个标签。标签分为5类,包括攻击和正常。的攻击类型分为四类:DOS(拒绝服务攻击),R2L(未经授权的访问从远程主),U2R(本地超级用户特权的未经授权的访问),和探针(端口监控或扫描)。正常是正常数据。</p> <p>第二个实验中使用的数据集是UNSW-NB15。数据收集在2015年澳大利亚的真实网络环境下网络安全中心(ACCS)。网络流量记录包含真正的正常活动和攻击行为。的网络记录该数据集包含49个网络特性包括一个类标签,并有10种网络包括正常行为和异常9入侵攻击。CICIDS2017和CICIDS2018数据集是最近的数据集,是由加拿大学院的网络安全。这两个数据集是接近真实的网络环境。CICIDS2017包含83原始功能。我们已经删除了一些特性,比如源和目标IP、ID、和时间戳,因为使用这个信息可能导致过度训练。最后,我们得到了一个包含80的特性和选择2515416样本数据集实验。CICIDS2018相似,我们也删除一些不必要的特性和选择一个公正的原始数据集的子集。 All datasets details are shown in Tables<a href="//www.newsama.com/journals/misy/2020/6633252/tab1/" target="_blank">1</a>- - - - - -<a href="//www.newsama.com/journals/misy/2020/6633252/tab5/" target="_blank">5</a>。</p> <div class="floats-partial-section partial-other-wrapper" data-section="tab1"> <div class="floats-partial-content fig-content-tab1"> <table class="table-group" id="tab1"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="4"> <hr></td> </tr> <tr class="thead"> <td class="align_left">数据集</td> <td class="align_center">实例</td> <td class="align_center">特性</td> <td class="align_center">类</td> </tr> <tr> <td class="thead-hr" colspan="4"> <hr></td> </tr> <tr> <td class="align_left">NSL-KDD</td> <td class="align_center">148517年</td> <td class="align_center">43</td> <td class="align_center">5</td> </tr> <tr> <td class="align_left">UNSW-NB15</td> <td class="align_center">257673年</td> <td class="align_center">49</td> <td class="align_center">10</td> </tr> <tr> <td class="align_left">CICIDS2017</td> <td class="align_center">2515416</td> <td class="align_center">80年</td> <td class="align_center">13</td> </tr> <tr> <td class="align_left">CICIDS2018</td> <td class="align_center">288909年</td> <td class="align_center">80年</td> <td class="align_center">15</td> </tr> <tr class="table-tr"> <td colspan="4"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表1</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/tab1/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 实验数据集的信息。</d我v> </div> </div> <div class="floats-partial-section partial-other-wrapper" data-section="tab2"> <div class="floats-partial-content fig-content-tab2"> <table class="table-group" id="tab2"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="7"> <hr></td> </tr> <tr class="thead"> <td class="align_left">火车/测试</td> <td class="align_center">总</td> <td class="align_center">正常的</td> <td class="align_center">DOS</td> <td class="align_center">U2R</td> <td class="align_center">R2L</td> <td class="align_center">探针</td> </tr> <tr> <td class="thead-hr" colspan="7"> <hr></td> </tr> <tr> <td class="align_left">训练集</td> <td class="align_center">125973年</td> <td class="align_center">67343年</td> <td class="align_center">45927年</td> <td class="align_center">11656年</td> <td class="align_center">995年</td> <td class="align_center">52</td> </tr> <tr> <td class="align_left">测试集</td> <td class="align_center">22544年</td> <td class="align_center">9711年</td> <td class="align_center">7458年</td> <td class="align_center">2421年</td> <td class="align_center">2754年</td> <td class="align_center">200年</td> </tr> <tr class="table-tr"> <td colspan="7"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表2</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/tab2/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 实验NSL-KDD信息。</d我v> </div> </div> <div class="floats-partial-section partial-other-wrapper" data-section="tab3"> <div class="floats-partial-content fig-content-tab3"> <table class="table-group" id="tab3"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="3"> <hr></td> </tr> <tr class="thead"> <td class="align_left">类</td> <td class="align_center">训练集</td> <td class="align_center">测试集</td> </tr> <tr> <td class="thead-hr" colspan="3"> <hr></td> </tr> <tr> <td class="align_left">正常的</td> <td class="align_center">56000年</td> <td class="align_center">37000年</td> </tr> <tr> <td class="align_left">分析</td> <td class="align_center">2000年</td> <td class="align_center">677年</td> </tr> <tr> <td class="align_left">后门</td> <td class="align_center">1746年</td> <td class="align_center">583年</td> </tr> <tr> <td class="align_left">DoS</td> <td class="align_center">12264年</td> <td class="align_center">4089年</td> </tr> <tr> <td class="align_left">利用</td> <td class="align_center">33393年</td> <td class="align_center">11132年</td> </tr> <tr> <td class="align_left">fuzz</td> <td class="align_center">18184年</td> <td class="align_center">6062年</td> </tr> <tr> <td class="align_left">通用的</td> <td class="align_center">40000年</td> <td class="align_center">18871年</td> </tr> <tr> <td class="align_left">侦察</td> <td class="align_center">10491年</td> <td class="align_center">3496年</td> </tr> <tr> <td class="align_left">Shellcode</td> <td class="align_center">1133年</td> <td class="align_center">378年</td> </tr> <tr> <td class="align_left">蠕虫</td> <td class="align_center">130年</td> <td class="align_center">44</td> </tr> <tr> <td class="align_left">总数量</td> <td class="align_center">175341年</td> <td class="align_center">82332年</td> </tr> <tr class="table-tr"> <td colspan="3"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表3</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/tab3/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 实验UNSW-NB15信息。</d我v> </div> </div> <div class="floats-partial-section partial-other-wrapper" data-section="tab4"> <div class="floats-partial-content fig-content-tab4"> <table class="table-group" id="tab4"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="3"> <hr></td> </tr> <tr class="thead"> <td class="align_left">类</td> <td class="align_center">数量</td> <td class="align_center">比例</td> </tr> <tr> <td class="thead-hr" colspan="3"> <hr></td> </tr> <tr> <td class="align_left">正常的</td> <td class="align_center">2089692</td> <td class="align_center">83.07</td> </tr> <tr> <td class="align_left">DoS绿巨人</td> <td class="align_center">172838年</td> <td class="align_center">6.87</td> </tr> <tr> <td class="align_left">端口搜索</td> <td class="align_center">128008年</td> <td class="align_center">5.08</td> </tr> <tr> <td class="align_left">分布式拒绝服务</td> <td class="align_center">90694年</td> <td class="align_center">3所示。6</td> </tr> <tr> <td class="align_left">Dos《黄金眼》在内的邦德系列</td> <td class="align_center">10283年</td> <td class="align_center">0.41</td> </tr> <tr> <td class="align_left">FTP-Patator</td> <td class="align_center">5931年</td> <td class="align_center">0.23</td> </tr> <tr> <td class="align_left">SSH-Patator</td> <td class="align_center">5385年</td> <td class="align_center">0.21</td> </tr> <tr> <td class="align_left">DoS Slowloris</td> <td class="align_center">5228年</td> <td class="align_center">0.2</td> </tr> <tr> <td class="align_left">DoS SlowHTTPTest</td> <td class="align_center">3219年</td> <td class="align_center">0.12</td> </tr> <tr> <td class="align_left">网络攻击</td> <td class="align_center">2143年</td> <td class="align_center">0.085</td> </tr> <tr> <td class="align_left">机器人</td> <td class="align_center">1948年</td> <td class="align_center">0.08</td> </tr> <tr> <td class="align_left">渗透</td> <td class="align_center">36</td> <td class="align_center">0.0014</td> </tr> <tr> <td class="align_left">Heartbleed</td> <td class="align_center">11</td> <td class="align_center">0.0004</td> </tr> <tr class="table-tr"> <td colspan="3"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表4</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/tab4/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 实验CICIDS2017信息。</d我v> </div> </div> <div class="floats-partial-section partial-other-wrapper" data-section="tab5"> <div class="floats-partial-content fig-content-tab5"> <table class="table-group" id="tab5"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="3"> <hr></td> </tr> <tr class="thead"> <td class="align_left">类</td> <td class="align_center">训练集</td> <td class="align_center">测试集</td> </tr> <tr> <td class="thead-hr" colspan="3"> <hr></td> </tr> <tr> <td class="align_left">正常的</td> <td class="align_center">50791年</td> <td class="align_center">12698年</td> </tr> <tr> <td class="align_left">SSH-BruteForce</td> <td class="align_center">184年</td> <td class="align_center">46</td> </tr> <tr> <td class="align_left">FTP-BruteForce</td> <td class="align_center">489年</td> <td class="align_center">122年</td> </tr> <tr> <td class="align_left">BruteForce-XSS</td> <td class="align_center">7504年</td> <td class="align_center">1876年</td> </tr> <tr> <td class="align_left">BruteForce-Web</td> <td class="align_center">15469年</td> <td class="align_center">3867年</td> </tr> <tr> <td class="align_left">SQL注入</td> <td class="align_center">70年</td> <td class="align_center">17</td> </tr> <tr> <td class="align_left">DoS attacks-Hulk</td> <td class="align_center">18667年</td> <td class="align_center">4667年</td> </tr> <tr> <td class="align_left">DoS attacks-SlowHTTPTest</td> <td class="align_center">55956年</td> <td class="align_center">13989年</td> </tr> <tr> <td class="align_left">DoS attacks-Slowloris</td> <td class="align_center">4396年</td> <td class="align_center">1099年</td> </tr> <tr> <td class="align_left">DoS attacks-GoldenEye</td> <td class="align_center">16603年</td> <td class="align_center">4151年</td> </tr> <tr> <td class="align_left">DDOS attack-HOIC</td> <td class="align_center">27441年</td> <td class="align_center">6860年</td> </tr> <tr> <td class="align_left">DDOS attack-LOIC-UDP</td> <td class="align_center">1384年</td> <td class="align_center">346年</td> </tr> <tr> <td class="align_left">DDOS attack-LOIC-HTTP</td> <td class="align_center">23048年</td> <td class="align_center">5762年</td> </tr> <tr> <td class="align_left">机器人</td> <td class="align_center">11448年</td> <td class="align_center">2862年</td> </tr> <tr> <td class="align_left">渗透</td> <td class="align_center">6478年</td> <td class="align_center">1620年</td> </tr> <tr> <td class="align_left">总数量</td> <td class="align_center">231127年</td> <td class="align_center">57782年</td> </tr> <tr class="table-tr"> <td colspan="3"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表5</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/tab5/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 实验CICIDS2018信息。</d我v> </div> </div> <p>四个数据集的特点是由许多数值特性和几个字符的特性字符不能直接用于拟议的入侵检测模型,和实验使用一个炎热的编码方法将从性格到号码。例如,第二列的NSL-KDD数据集“protcol_type”包含三个不同的价值观:“tcp、udp,”和“icmp,”和编码代表他们[0,0,1],(0,1,0),(1,0,0)。编码后,数据规范化,避免值之间的大小关系会影响训练的结果,和所有的特征值映射到区间[0,1]。<span class="equation_break" id="EEq10"><span class="left_break" id="L41"></span><span class="middle_break"><span class="center_break" id="M41"> <svg height="29.6341pt" id="M41-1" version="1.1" viewbox="0 0 124.584 29.6341" width="124.584pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B41-1"> <g 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transform="matrix(.013,0,0,-0.013,13.29,9.25)"> <use xlink:href="#g190-42"></use> </g> </g> </svg></span></span></p> <p>这些数据将不参与决策树的建立,可以代替验证集的验证模型。</p> <p>OOB误码率计算来评估不同的滑动窗口大小对模型的影响。从图可以看出<a href="//www.newsama.com/journals/misy/2020/6633252/fig6/" target="_blank">6</a>,当窗口大小<我>d /</我>4,平均错误率最低,平均OOB出错率是最高的<我>d /</我>16,<我>d</我>是长度的原始特征。这个结果解释,更细粒度的窗口大小不一定是更好的在提高模型的泛化性能。与决策树的增加,错误率开始收敛在0.06左右。所以,最后建议<我>d</我>/ 4是合适的大小。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig6"> <div class="floats-partial-content fig-content-fig6"> <div class="partial-carousel--single-image"> <img alt="" name="6633252.fig.006" src="https://static-01.hindawi.com/articles/misy/volume-2020/6633252/figures/6633252.fig.006.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图6</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/fig6/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 功能块窗口大小的影响。</d我v> </div> </div> <p>然后,通过使用10倍交叉验证其余的参数调整。例如,n_estimators是决策树的数量。一般来说,更多的树木使模型更加健壮和更好的性能。考虑一个更广泛的可用性,我们搜索的范围(0,500]步长50和比较结果后10倍交叉验证最优值。最后,表<a href="//www.newsama.com/journals/misy/2020/6633252/tab6/" target="_blank">6</a>总结了hyperparameters FS-DPRF设置。</p> <div class="floats-partial-section partial-other-wrapper" data-section="tab6"> <div class="floats-partial-content fig-content-tab6"> <table class="table-group" id="tab6"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="3"> <hr></td> </tr> <tr class="thead"> <td class="align_left">参数</td> <td class="align_center">Feature-grained随机森林</td> <td class="align_center">级联层随机森林</td> </tr> <tr> <td class="thead-hr" colspan="3"> <hr></td> </tr> <tr> <td class="align_left"><i>n</我>_estimators</td> <td class="align_center">150年</td> <td class="align_center">350年</td> </tr> <tr> <td class="align_left">max_depth</td> <td class="align_center">10</td> <td class="align_center">15</td> </tr> <tr> <td class="align_left">max_features</td> <td class="align_center"><span style="width: 16.6193ptpx;"> <svg height="13.1484pt" id="M45" style="vertical-align:-2.636801pt" version="1.1" viewbox="-0.0498162 -10.5116 16.6193 13.1484" width="16.6193pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g transform="matrix(.013,0,0,-0.013,0,-.271)"> <path d="M782 731H740L476 -79H474L280 352L133 274L151 239L242 286L473 -219L782 731Z"></path> </g> <rect height="0.65243" width="6.55046" x="9.96925" y="-9.80939"></rect> <g transform="matrix(.013,0,0,-0.013,9.969,0)"> <path d="M495 86L479 114C446 82 419 66 409 66C401 66 401 72 406 97C420 166 436 231 453 297C489 435 454 448 428 448C406 448 384 439 354 422C305 394 222 327 161 247H159L183 345C200 415 194 448 173 448C143 448 82 410 23 351L38 325C64 349 95 371 105 371C111 371 116 365 109 336L25 -4L31 -12C50 -4 77 3 107 9C119 69 132 122 145 168C197 254 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<g transform="matrix(.013,0,0,-0.013,49.886,0)"> <path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"></path> </g> </svg></span></td> </tr> <tr> <td class="align_left">min_samples_leaf</td> <td class="align_center">4</td> <td class="align_center">2</td> </tr> <tr> <td class="align_left">min_samples_split</td> <td class="align_center">2</td> <td class="align_center">2</td> </tr> <tr> <td class="align_left">标准</td> <td class="align_center">基尼</td> <td class="align_center">基尼</td> </tr> <tr class="table-tr"> <td colspan="3"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表6</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/tab6/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> FS-DPRF hyper-parameter设置。</d我v> </div> </div> <h5 id="sec6.3">6.3。模型评价</h5> <p>在本节中,我们比较FS-DPRF和并行随机森林(脉冲),DSSVM [<a href="#B17">17</a>],A-DNN [<a href="#B19">19</a>]。分类性能测量的精度,精度和召回F1,检出率(博士),和假警报率(远)。F1的分数是一个评价指标,综合考虑召回率和精度,及其定义见方程(<a href="#EEq12">13</a>)。F1的分数越高价值意味着更好的算法的分类性能。评价指标定义如下:<span class="equation_break" id="EEq12"><span class="left_break" id="L47"></span><span class="middle_break"><span class="center_break" id="M47"> <svg height="208.719pt" id="M47-1" version="1.1" viewbox="0 0 48.847 208.719" width="48.847pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B47-1"> <g transform="matrix(.013,0,0,-0.013,0,17.261)"> <path d="M673 0V28C608 34 594 43 563 129C499 303 432 494 370 665L339 656L132 132C98 43 84 37 20 28V0H238V28C162 35 154 45 175 108C188 150 203 192 219 237H432C454 176 474 122 487 83S491 36 428 28V0H673ZM418 280H234C265 362 296 450 328 535H330L418 280Z" id="g190-66"></path> </g> <g transform="matrix(.013,0,0,-0.013,8.65,17.261)"> 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xlink:href="#g190-100"></use> </g> <g transform="matrix(.013,0,0,-0.013,104.55,205.551)"> <use xlink:href="#g190-98"></use> </g> <g transform="matrix(.013,0,0,-0.013,110.33,205.551)"> <use xlink:href="#g190-109"></use> </g> <g transform="matrix(.013,0,0,-0.013,113.68,205.551)"> <use xlink:href="#g190-109"></use> </g> <g transform="matrix(.013,0,0,-0.013,118.3,196.326)"> <use xlink:href="#g113-45"></use> </g> </g> </svg></span></span><span class="right_break" id="R47"> <svg class="label" height="11.44pt" id="M47-7" version="1.1" viewbox="0 0 19.468 11.44" width="19.468pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B47-7"> <g transform="matrix(.013,0,0,-0.013,0,9.26)"> <use xlink:href="#g190-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,4.5,9.26)"> <use xlink:href="#g190-213"></use> </g> <g transform="matrix(.013,0,0,-0.013,8.93,9.26)"> <use xlink:href="#g190-243"></use> </g> <g transform="matrix(.013,0,0,-0.013,14.97,9.26)"> <use xlink:href="#g190-42"></use> </g> </g> </svg> <svg class="label" height="11.44pt" id="M47-10" version="1.1" viewbox="0 0 19.258 11.44" width="19.258pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B47-10"> <g transform="matrix(.013,0,0,-0.013,0,9.26)"> <use xlink:href="#g190-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,4.49,9.26)"> <use xlink:href="#g190-213"></use> </g> <g transform="matrix(.013,0,0,-0.013,8.89,9.26)"> <use xlink:href="#g190-232"></use> </g> <g transform="matrix(.013,0,0,-0.013,14.76,9.26)"> <use xlink:href="#g190-42"></use> </g> </g> </svg></span></span>其中TP表示真正的攻击数量预测攻击类型,FN代表真正的攻击预测正常数量,《外交政策》代表了真正的正常预测攻击类型,数量和TN代表正常正常数量的预测。比较结果的平均值得到十个实验在不同的数据集。为了验证模型的性能在两个分类:正常和攻击。在本部分中,所有攻击类型被视为异常类型。我们所有的数据标记为两种类型:正常和异常。表<a href="//www.newsama.com/journals/misy/2020/6633252/tab7/" target="_blank">7</a>显示了正常和异常的比较结果在给定的数据集。</p> <div class="floats-partial-section partial-other-wrapper" data-section="tab7"> <div class="floats-partial-content fig-content-tab7"> <table class="table-group" id="tab7"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="5"> <hr></td> </tr> <tr class="thead"> <td class="align_left">数据集</td> <td class="align_center">算法</td> <td class="align_center">精度</td> <td class="align_center">精度</td> <td class="align_center">回忆</td> </tr> <tr> <td class="thead-hr" colspan="5"> <hr></td> </tr> <tr> <td class="align_left" rowspan="4">NSL-KDD</td> <td class="align_center">脉冲重复频率</td> <td class="align_center">0.93224</td> <td class="align_center">0.94648</td> <td class="align_center">0.93013</td> </tr> <tr> <td class="align_center">DSSVM</td> <td class="align_center">0.94137</td> <td class="align_center">0.95763</td> <td class="align_center">0.95065</td> </tr> <tr> <td class="align_center">A-DNN</td> <td class="align_center">0.98952</td> <td class="align_center">0.98101</td> <td class="align_center">0.97474</td> </tr> <tr> <td class="align_center">FS-DPRF</td> <td class="align_center">0.99132</td> <td class="align_center">0.99213</td> <td class="align_center">0.98733</td> </tr> <tr> <td class="align_left" colspan="5"> <hr></td> </tr> <tr> <td class="align_left" rowspan="4">UNSW-NB15</td> <td class="align_center">脉冲重复频率</td> <td class="align_center">0.85321</td> <td class="align_center">0.93752</td> <td class="align_center">0.87308</td> </tr> <tr> <td class="align_center">DSSVM</td> <td class="align_center">0.81927</td> <td class="align_center">0.92931</td> <td class="align_center">0.86054</td> </tr> <tr> <td class="align_center">A-DNN</td> <td class="align_center">0.94219</td> <td class="align_center">0.95740</td> <td class="align_center">0.93829</td> </tr> <tr> <td class="align_center">FS-DPRF</td> <td class="align_center">0.97763</td> <td class="align_center">0.98363</td> <td class="align_center">0.96401</td> </tr> <tr> <td class="align_left" colspan="5"> <hr></td> </tr> <tr> <td class="align_left" rowspan="4">CICIDS2017</td> <td class="align_center">脉冲重复频率</td> <td class="align_center">0.86532</td> <td class="align_center">0.85655</td> <td class="align_center">0.86227</td> </tr> <tr> <td class="align_center">DSSVM</td> <td class="align_center">0.85290</td> <td class="align_center">0.84739</td> <td class="align_center">0.84075</td> </tr> <tr> <td class="align_center">A-DNN</td> <td class="align_center">0.96527</td> <td class="align_center">0.96028</td> <td class="align_center">0.94272</td> </tr> <tr> <td class="align_center">FS-DPRF</td> <td class="align_center">0.97430</td> <td class="align_center">0.95330</td> <td class="align_center">0.97931</td> </tr> <tr> <td class="align_left" colspan="5"> <hr></td> </tr> <tr> <td class="align_left" rowspan="4">CICIDS2018</td> <td class="align_center">脉冲重复频率</td> <td class="align_center">0.89876</td> <td class="align_center">0.91010</td> <td class="align_center">0.90733</td> </tr> <tr> <td class="align_center">DSSVM</td> <td class="align_center">0.87852</td> <td class="align_center">0.89110</td> <td class="align_center">0.88031</td> </tr> <tr> <td class="align_center">A-DNN</td> <td class="align_center">0.94480</td> <td class="align_center">0.94754</td> <td class="align_center">0.93015</td> </tr> <tr> <td class="align_center">FS-DPRF</td> <td class="align_center">0.96750</td> <td class="align_center">0.96031</td> <td class="align_center">0.95424</td> </tr> <tr class="table-tr"> <td colspan="5"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表7</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/tab7/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 四个给定的数据集上的实验结果。</d我v> </div> </div> <p>在NSL-KDD数据集比较算法的准确性超过90%,和深层神经网络取得了98%的好成绩,但它仍然是比FS-DPRF低了1%。同样,精度和召回反映,我们的模型确认正常网络行为的能力是最好的。UNSW-NB15数据集,浅机器学习算法开始执行薄弱,只有80%的准确率-85%。这两种方法基于深度学习携带高准确率在94.2%和97.7%以上。FS-DPRF比神经网络。数据变得越来越复杂和更接近真实的网络环境,浅的平均精度算法的两个数据集CICIDS2017 CICIDS2018已经低于90%。精度和召回也失去了竞争力。A-DNN的准确率和FS-DPRF CICIDS2017数据集达到96.5%和97.4%,分别。虽然A-DNN的精度较高,FS-DPRF领先1%,召回率较高。召回率越高,攻击是预测的概率越高,这意味着FS-DPRF对CICIDS2017有更好的攻击检测能力。 The last group of data is the result of the CICDIS2018. The accuracy of FS-DPRF is 3% higher than that of A-DNN, and the precision and recall rate are also higher than A-DNN by 1.3% and 2.4%, respectively.</p> <p>图<a href="//www.newsama.com/journals/misy/2020/6633252/fig7/" target="_blank">7</a>显示FS-DPRF的比较结果,平行随机森林,DSSVM,深层神经网络在F1指数。NSL-KDD数据集上的新方法的得分高于浅机器学习,3% - -5%的一个优势,这也是比A-DNN高出1%。UNSW-NB15数据集上的结果表明,有一个深的性能差距上优于方法和脉冲重复频率和DSSVM。基于深度学习方法有更好的表现,和FS-DPRF的得分是3%高于深层神经网络。CICIDS2017和CICIDS2018 F1分数的四个方法略低于前两组的实验数据,但是基于深层学习方法仍然保持领先脉冲重复频率和DSSVM。F1的FS-DPRF A-DNN CICIDS2017比这高出1%,和F1得分FS-DPRF CICIDS2018 A-DNN高出1.9%。F1的分数指数这四个数据集显示与森林有关的深度学习网络在正常/异常两类分类实验比肤浅的机器学习方法和与深层神经网络相比具有竞争力。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig7"> <div class="floats-partial-content fig-content-fig7"> <div class="partial-carousel--single-image"> <img alt="" name="6633252.fig.007" src="https://static-01.hindawi.com/articles/misy/volume-2020/6633252/figures/6633252.fig.007.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图7</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/fig7/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 比较之间的F1评分模型和以前的经典算法。</d我v> </div> </div> <p>为了验证模型的检测能力在multiclassification,我们做了另一组实验数据集NSL-KDD。根据原来的标签,所有数据分为五类,如表所示<a href="//www.newsama.com/journals/misy/2020/6633252/tab8/" target="_blank">8</a>。数据预处理和参数设置是一样的前面的部分,和十个实验进行平均。值得注意的是,考虑到支持向量机的二叉分类限制,我们花了很多标签博士测试工作,在“一定的攻击/其他”分开。从实验结果可以看出表<a href="//www.newsama.com/journals/misy/2020/6633252/tab8/" target="_blank">8</a>本文的方法提高了检出率5类标签,和FR相对较低。尽管U2R攻击类型的检测结果不是特别理想,这也是相关类别的分布不平衡的数据集。总之,本文中的方法显示多级最好的力量攻击和正常的分类实验。</p> <div class="floats-partial-section partial-other-wrapper" data-section="tab8"> <div class="floats-partial-content fig-content-tab8"> <table class="table-group" id="tab8"> <tbody> <tr> <td> <table class="table"> <tbody> <tr> <td class="thead-hr" colspan="9"> <hr></td> </tr> <tr class="thead"> <td class="align_left" rowspan="2">类型</td> <td class="align_center" colspan="2">脉冲重复频率</td> <td class="align_center" colspan="2">DSSVM</td> <td class="align_center" colspan="2">A-DNN</td> <td class="align_center" colspan="2">FS-DPRF</td> </tr> <tr class="thead"> <td class="align_center">博士</td> <td class="align_center">远</td> <td class="align_center">博士</td> <td class="align_center">远</td> <td class="align_center">博士</td> <td class="align_center">远</td> <td class="align_center">博士</td> <td class="align_center">远</td> </tr> <tr> <td class="thead-hr" colspan="9"> <hr></td> </tr> <tr> <td class="align_left">DOS</td> <td class="align_center">0.942</td> <td class="align_center">0.057</td> <td class="align_center">0.960</td> <td class="align_center">0.040</td> <td class="align_center">0.983</td> <td class="align_center">0.017</td> <td class="align_center">0.990</td> <td class="align_center">0.010</td> </tr> <tr> <td class="align_left">R2L</td> <td class="align_center">0.947</td> <td class="align_center">0.053</td> <td class="align_center">0.892</td> <td class="align_center">0.109</td> <td class="align_center">0.924</td> <td class="align_center">0.076</td> <td class="align_center">0.961</td> <td class="align_center">0.039</td> </tr> <tr> <td class="align_left">U2R</td> <td class="align_center">0.930</td> <td class="align_center">0.073</td> <td class="align_center">0.921</td> <td class="align_center">0.078</td> <td class="align_center">0.925</td> <td class="align_center">0.075</td> <td class="align_center">0.976</td> <td class="align_center">0.024</td> </tr> <tr> <td class="align_left">探针</td> <td class="align_center">0.863</td> <td class="align_center">0.136</td> <td class="align_center">0.911</td> <td class="align_center">0.088</td> <td class="align_center">0.887</td> <td class="align_center">0.113</td> <td class="align_center">0.946</td> <td class="align_center">0.053</td> </tr> <tr> <td class="align_left">正常的</td> <td class="align_center">0.955</td> <td class="align_center">0.048</td> <td class="align_center">0.960</td> <td class="align_center">0.040</td> <td class="align_center">0.948</td> <td class="align_center">0.052</td> <td class="align_center">0.983</td> <td class="align_center">0.016</td> </tr> <tr class="table-tr"> <td colspan="9"> <hr class="tbody-hr"></td> </tr> </tbody> </table></td> </tr> </tbody> </table> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">表8</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/tab8/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> NSL-KDD multiclassification结果。</d我v> </div> </div> <p>然后我们测试平均执行时间和加速我们的模型,用于衡量并行集群的可伸缩性。加速被定义为<span class="equation_break" id="EEq14"><span class="left_break" id="L48"></span><span class="middle_break"><span class="center_break" id="M48"> <svg height="30.1117pt" id="M48-1" version="1.1" viewbox="0 0 45.654 30.1117" width="45.654pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B48-1"> <g transform="matrix(.013,0,0,-0.013,0,17.705)"> <use xlink:href="#g113-84"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,6.07,20.836)"> <path d="M573 302C573 402 527 451 414 451C386 451 343 446 313 437L330 513L320 522C295 508 261 484 243 463L230 415C194 400 159 383 126 359L131 330C159 344 187 357 222 368L109 -147C96 -204 80 -214 18 -223L13 -255L256 -244L259 -212L236 -210C184 -205 180 -195 191 -141L219 -1C240 -10 268 -12 284 -12C352 4 431 48 484 104C543 166 573 240 573 302ZM481 290C481 165 381 37 305 37C280 37 249 56 235 71L302 395C328 399 353 402 372 402C427 402 481 378 481 290Z" id="g50-113"></path> </g> <g transform="matrix(.013,0,0,-0.013,15.66,17.705)"> <use xlink:href="#g117-34"></use> </g> <g transform="matrix(.013,0,0,-0.013,28.73,8.962)"> <use xlink:href="#g113-85"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,35.91,12.094)"> <use xlink:href="#g50-50"></use> </g> <rect height="0.65243" width="13.3824" x="28.12" y="14.051"></rect> <g transform="matrix(.013,0,0,-0.013,28.12,26.93)"> <use xlink:href="#g113-85"></use> </g> <g transform="matrix(.0091,0,0,-0.0091,35.29,30.062)"> <path d="M622 479C622 589 545 650 400 650H146L140 618C229 609 230 602 215 527L135 132C117 43 112 41 27 32L18 0H305L313 32C213 40 205 44 222 132L251 284L324 262H365C484 269 622 333 622 479ZM528 477C528 356 420 305 344 305C300 305 276 314 259 321L303 554C311 597 313 600 331 606C345 611 361 614 379 614C463 614 528 580 528 477Z" id="g50-81"></path> </g> <g transform="matrix(.013,0,0,-0.013,42.69,17.705)"> <use xlink:href="#g113-45"></use> </g> </g> </svg></span></span><span class="right_break" id="R48"> <svg class="label" height="11.44pt" id="M48-2" version="1.1" viewbox="0 0 19.638 11.44" width="19.638pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"> <g id="B48-2"> <g transform="matrix(.013,0,0,-0.013,0,9.26)"> <use xlink:href="#g190-41"></use> </g> <g transform="matrix(.013,0,0,-0.013,4.5,9.26)"> <use xlink:href="#g190-213"></use> </g> <g transform="matrix(.013,0,0,-0.013,8.68,9.26)"> <use xlink:href="#g190-166"></use> </g> <g transform="matrix(.013,0,0,-0.013,15.14,9.26)"> <use xlink:href="#g190-42"></use> </g> </g> </svg></span></span>在哪里<我>p</我>cpu的数量,<我>T</我><sub>1</sub>指的是顺序执行算法的执行时间<我>T</我><sub><i>p</我></sub>代表的执行时间<我>p</我>节点并行算法。从图可以看出<a href="//www.newsama.com/journals/misy/2020/6633252/fig8/" target="_blank">8</a>,从节点的增加,模型在四个给定数据集的平均执行时间是减少。执行时间的下降趋势在不同的数据集是不完全相同的由于数据大小。集群节点的平均执行时间和数量在所有情况下都表现出很强的相关性,这表明该方法具有良好的可伸缩性。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig8"> <div class="floats-partial-content fig-content-fig8"> <div class="partial-carousel--single-image"> <img alt="" name="6633252.fig.008" src="https://static-01.hindawi.com/articles/misy/volume-2020/6633252/figures/6633252.fig.008.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图8</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/fig8/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 平均执行时间FS-DPRF尺度在不同的节点。</d我v> </div> </div> <p>加速实验测试该模型在不同数量的奴隶节点。如图<a href="//www.newsama.com/journals/misy/2020/6633252/fig9/" target="_blank">9</a>,每个数据集所有的加速增加经常当节点数的增加,从1到25和倾向于慢下来的节点数量从25到50。结果表明,该模型具有良好的加速性能数据集不同的数量和尺寸。但是,它不显示一个完美的线性增长与上面所示的定义一样,它可以被通信开销和任务调度成本将成为更大的集群规模增加。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig9"> <div class="floats-partial-content fig-content-fig9"> <div class="partial-carousel--single-image"> <img alt="" name="6633252.fig.009" src="https://static-01.hindawi.com/articles/misy/volume-2020/6633252/figures/6633252.fig.009.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图9</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/fig9/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 加速FS-DPRF曲线在不同的节点上。</d我v> </div> </div> <p>最后,我们设置20从节点之间的缓存性能实验和比较火花的默认FS-DPRF LRU算法和优化方法。从图可以看出<a href="//www.newsama.com/journals/misy/2020/6633252/fig10/" target="_blank">10</a>FS-DPRF已经减少执行时间,时间在NSL-KDD减少8.8%和8.9%相比,UNSW-NB15 LRU。的执行时间FS-DPRF CICIDS2017和CICIDS2018也减少了7.2%和13.3%,分别与LRU相比。列图表显示缓存替换策略提出了可以减少成功异常检测任务的执行时间。即使效率排序和分区完整性检查在替换牺牲内存的一部分,如图<a href="//www.newsama.com/journals/misy/2020/6633252/fig11/" target="_blank">11</a>,FS-DPRF有点高于LRU算法在内存占用率,和可接受的实时入侵检测性能更重要。因此,优化并行化成功提高了任务执行的效率提出了入侵检测模型。</p> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig10"> <div class="floats-partial-content fig-content-fig10"> <div class="partial-carousel--single-image"> <img alt="" name="6633252.fig.0010" src="https://static-01.hindawi.com/articles/misy/volume-2020/6633252/figures/6633252.fig.0010.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图10</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/fig10/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 执行时间对比FS-DPRF和LRU。</d我v> </div> </div> <div class="floats-partial-section partial-carousel-wrapper" data-section="fig11"> <div class="floats-partial-content fig-content-fig11"> <div class="partial-carousel--single-image"> <img alt="" name="6633252.fig.0011" src="https://static-01.hindawi.com/articles/misy/volume-2020/6633252/figures/6633252.fig.0011.svgz"> <br> <strong></strong> </div> </div> <div class="floats-partial-footer"> <div class="floats-partial-caption"> <span class="caption-text">图11</span> <a aria-label="full view" class="caption-partial-url" href="//www.newsama.com/journals/misy/2020/6633252/fig11/" title="" target="_blank"></a> </div> <div class="floats-partial-caption-text"> 内存使用量比较FS-DPRF和LRU之间。</d我v> </div> </div> <h4 id="limitations-of-the-research">7所示。研究的局限性</h4> <p>研究的局限性是,模型将消耗大量的内存,所以得到一个训练有素的入侵检测模型需要强大的计算设备。虽然本文提出的模型的训练,取得了满意的效果cpu在火花的分布式环境中,不幸的是,目前的结构是自然不适合gpu。这使得模型暂时无法更好的加速在gpu上像一个深层神经网络。</p> <h4 id="conclusion-and-future-work">8。结论和未来的工作</h4> <p>从前辈的工作,整体上优于方法已经显示出令人信服的表演具有挑战性的任务,可以抽象地理解为分类问题。的主要科学贡献本文提出一个基于整体决策树的深度学习模型。灵感来自深层神经网络中,我们使用层组成的随机森林模仿隐藏层和完全连接层神经网络建立级联模型的随机森林。该模型利用滑动窗口来细分成许多小块样品特性,可以减少计算量的高维度数据为每个计算并保持原始功能的完整性。级联结构提高泛化能力,更高的准确率。模型只有少数hyperparameters而实现良好的泛化能力。另一部分的贡献在于,提出一个缓存替换策略引发环境并确定抽样的抽样加载的优先级顺序通过计算权重和完整性。它有效地降低了入侵检测任务的平均执行时间在分布式集群。四个数据集的实验结果表明,本文提出的模型执行比F1-measure并行随机森林和支持向量机的性能和准确性,实现竞争力的最先进的方法相比,神经网络。虽然该模型降低了平均执行时间,它增加了内存消耗和暂时不支持GPU加速。 Therefore, the model is more suitable for deployment on a distributed cluster with sufficient memory, which also reflects the limitations of our model. In the future, the work will focus more on the optimization processes of the features of training data to improve the prediction accuracy and will further research on the issue of unbalanced data distribution in the intrusion detection task.</p> <h4 id="data-availability">数据可用性</h4> <p>可用的数据集<a href="https://www.unb.ca/cic/datasets/index.html" target="_blank">https://www.unb.ca/cic/datasets/index.html</a>澳大利亚中心,网络范围的实验室网络安全(ACCS) (<a href="https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/" target="_blank">https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/</a>),和加拿大的网络安全研究所(<a href="https://www.unb.ca/cic/datasets/ids-2017.html%20https://www.unb.ca/cic/datasets/ids-2018.html" target="_blank">https://www.unb.ca/cic/datasets/ids - 2017. - html https://www.unb.ca/cic/datasets/ids - 2018. - html</a>)。</p> <h4 id="conflicts-of-interest">的利益冲突</h4> <p>作者宣称没有利益冲突。</p> <h4 id="acknowledgments">确认</h4> <p>这项工作是河北省自然科学基金的支持下,中国(F2019201427)和中国教育部基金项目(2017 a20004)。</p> </div> <div class="xml-content"> <h4 class="references" id="references">引用</h4> <ol> <li id="B1">m·艾哈迈德·A·n·马哈茂德和m . r .伊斯兰教”的调查异常检测技术在金融领域,“<我>未来一代计算机系统</我>,55卷,第288 - 278页,2016年。<span class="reflinks">视图:<a href="https://doi.org/10.1016/j.future.2015.01.001">出版商的网站</a><span class="sep">|</span><a href="https://scholar.google.com/scholar_lookup?title=A%20survey%20of%20anomaly%20detection%20techniques%20in%20financial%20domain&author=M. 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Ghorbani" target="_blank">谷歌学术搜索</a></span></li> </ol> </div> <div class="xml-content"> <h4 class="copyright" id="copyright">版权</h4> <p>版权©2020振鹏刘et al。这是一个开放分布式下文章<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">知识共享归属许可</a>,它允许无限制的使用、分配和复制在任何媒介,提供最初的工作是正确引用。</p> </div> </div> </article> </div> </div> </div> <div class="biblio__StyledBibblio-sc-1nou1lz-0 eqMMLR"> <div id="takeover" class="biblio__TakeoverWrapper-sc-1nou1lz-2 cITjcF"> <div id="bib--hide-takeover-element-mobile"> <div id="bib--takeover-element-mobile"> <h4 class="biblio__H4-sc-1nou1lz-4 lhUKwc">相关文章</h4> <div id="bib--rcm-takeover-element-mobile"> 对本文没有相关内容可用。</d我v> </div> </div> </div> </div> <div class="biblio__StyledBibblio-sc-1nou1lz-0 eqMMLR"> <div class="biblio__FooterWrapper-sc-1nou1lz-3 etZutQ mobile-widget" id="related-articles-hidden"> <div id="bib--hide-footer-element-mobile"> <hr class="biblio__HR-sc-1nou1lz-5 bkemVQ"> <h4 class="biblio__H4-sc-1nou1lz-4 lhUKwc">更多相关文章</h4> <div 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height="24"><span class="sc-dRFtgE jrsDZP">下载其他格式</span><span class="sc-gkFcWv gWTCXQ">更多的</span></span> <span class="sc-htoDjs bMdQcY sc-gbOuXE jGPvPL icon-colored _1CwL9R82TPl2HxDleeYGDR icon-themeable" title="" font-size="11"></span> </div> <div class="sc-fnwBNb etzuBr"> <a class="sc-htpNat bUhGXt link sc-iNhVCk gWgCNb open_panel" href="//www.newsama.com/downloads/journals/misy/2020/6633252.epub" aria-label="ePub" property="background-color" color="#D8D8D8"> <div class="sc-eAKXzc WHkfb"> ePub</d我v><img alt="" class="sc-EHOje jOLhQl sc-bfYoXt cGuwDn" title="" role="presentation" 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height="24"><span class="sc-dRFtgE jrsDZP">订单打印副本</span><span class="sc-gkFcWv gWTCXQ">订单</span></span><span class="sc-htoDjs kNoiZu sc-gbOuXE jGPvPL right__icon icon-colored _32KP5uexH-Bgs7Kj8Gk1Tz icon-themeable" title="" font-size="16"></span></a> <div class="sc-hBbWxd kdRRST"> <div class="sc-dyGzUR cHatnS"> <div> 的观点<span>967年</span> </div> <div> 下载<span>588年</span> </div> </div> <div> <div class="sc-drKuOJ kxdGAZ"> <span class="__dimensions_badge_embed__" data-doi="10.1155/2020/6633252" data-style="small_circle"></span>引用</d我v> </div> </div> <nav class="sc-bRBYWo fXBKFa sc-hUfwpO jHPgUP" id="toolbar__iconNav"> <h3 class="sc-Rmtcm hNPrIr">分享文章</h3> <ul class="sc-hzDkRC bnbfTS"> <li class="iconBorder"><a class="sc-htpNat bUhGXt link" href="https://twitter.com/share?url=https://doi.org/10.1155/2020/6633252&via=Hindawi" aria-label="Sharing on Twitter" rel="noopener noreferrer" target="_blank"><img alt="" class="sc-EHOje jOLhQl socialTwitter" title="" role="presentation" 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