TY -的A2 -彭,浩盟——王,志强盟,任Xiaorui AU - Li Shuhao AU - Wang Bingyan盟——张,建议非盟-杨,道PY - 2021 DA - 2021/05/15 TI -恶意URL基于卷积神经网络检测模型SP - 5518528六世- 2021 AB -随着互联网技术的发展,网络安全受到各种威胁。特别是,攻击者可以传播恶意统一资源定位器(URL)进行攻击,如钓鱼和垃圾邮件。研究恶意URL为防御这些攻击检测具有重要意义。然而,在当前的研究中仍存在一些问题。例如,恶意功能不能有效地提取。一些现有的检测方法很容易被攻击者逃避。我们设计一个恶意URL基于动态卷积神经网络检测模型(DCNN)来解决这些问题。一个新的折叠层添加到原来的多层卷积网络。它取代了池层与k-max-pooling层。在动态卷积算法,中间特性映射层的宽度取决于输入维向量。 Moreover, the pooling layer parameters are dynamically adjusted according to the length of the URL input and the depth of the current convolution layer, which is beneficial to extracting more in-depth features in a wider range. In this paper, we propose a new embedding method in which word embedding based on character embedding is leveraged to learn the vector representation of a URL. Meanwhile, we conduct two groups of comparative experiments. First, we conduct three contrast experiments, which adopt the same network structure and different embedding methods. The results prove that word embedding based on character embedding can achieve higher accuracy. We then conduct the other three experiences, which use the same embedding method proposed in this paper and use different network structures to determine which network is most suitable for our model. We verify that the model designed in this paper has the highest accuracy (98%) in detecting malicious URL through these experiences. SN - 1939-0114 UR - https://doi.org/10.1155/2021/5518528 DO - 10.1155/2021/5518528 JF - Security and Communication Networks PB - Hindawi KW - ER -