北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (5): 87-92.

• 论文 • 上一篇    下一篇

一种基于多层次特征提取的DGA域名检测方法

杨宏宇1,章涛1,胡泽1,张良2,谢丽霞1   

  1. 1. 中国民航大学
    2. University of Arizona
  • 收稿日期:2023-08-29 修回日期:2023-12-16 出版日期:2024-10-28 发布日期:2024-11-10
  • 通讯作者: 胡泽 E-mail:zhu@cauc.edu.cn
  • 基金资助:
    国家自然科学基金项目;国家自然科学基金民航联合基金项目;中国民航大学信息安全测评中心开放基金项目;中央高校基本科研业务费项目

A DGA Domain Name Detection Method Based on Multi-level Feature Extraction

  • Received:2023-08-29 Revised:2023-12-16 Online:2024-10-28 Published:2024-11-10

摘要: 针对现有关于域名生成算法(DGA, domain generate algorithm)的域名检测方法无法充分提取并利用域名特征,且基于词嵌入的检测方法易造成重要信息丢失等不足,提出一种基于多层次特征提取的DGA域名检测方法(DDMFE, a DGA domain name detection method based on multi-level feature extraction)。首先,利用词嵌入方法生成域名向量表示,同时提取域名字符特征,得到域名字符特征。其次,使用多层次特征提取网络处理域名向量,捕获域名的上下文信息与语义信息,并融合不同的域名信息生成域名文本级别的特征表示。最后,为计算域名分类概率,使用前馈神经网络处理域名字符特征,使用改进的胶囊网络处理域名文本特征,并采用融合操作生成域名的检测概率向量实现域名检测。经实验验证,所提方法在DGA域名检测和DGA算法识别方面的准确率分别提高了1.1%~8.6%、1.8%~3.1%,具有较好的检测性能。

关键词: DGA域名检测, 多头金字塔网络, 字符特征, 胶囊网络

Abstract: To tackle the problems that the existing domain detection methods of domain generation algorithm (DGA) cannot fully extract and utilize the domain features and the detection methods based on word embedding are prone to cause the loss of important information, a DGA domain name detection method based on multi-level feature extraction (DDMFE) is proposed. Firstly, the vector representations of domains are obtained by word embedding, and the domain character features are extracted to obtain preprocessing samples. Secondly, the domain vectors are processed by a multi-level feature extraction network to capture the contextual and semantic information of the domains and fuse different domain information to generate a text-level feature representation of the domains. Finally, to calculate the domain classification probability, a feed-forward neural network is used to process the domain character features, an improved capsule network is used to process the domain text features, and a fusion operation is used to generate the domain classification probability for domain detection. After experimental validation, the proposed method improves the accuracy of DGA domain name detection and DGA algorithm recognition by 1.1%~8.6% and 1.8%~3.1%, respectively, which provides a good detection performance.

Key words: DGA domain name detection, multi-head pyramid network, character features, capsule network

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