HAGDetector: Heterogeneous DGA domain name detection model. Issue 120 (September 2022)

• Record Type: Journal Article
• Authors: Liang, Jianbing; Chen, Shuhui; Wei, Ziling; Zhao, Shuang; Zhao, Wei
• Is Part Of: Computers & security. Issue 120(2022)
• Publication Date: 2022-09
• Subjects: Network security -- Convolutional neural network -- Domain generation algorithm -- Machine learning -- Right shifted tensor (RST) -- DGA Detection; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2022.102803
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: The botnet relies on the Command and Control (C&C) channels to conduct its malicious activities remotely. The Domain Generation Algorithm (DGA) is often used by botnets to hide their Command and Control (C&C) server and evade take-down attempts, which allows the bot to generate a large number of domain names until it finds its C&C server. The lengths of domain names generated by DGAs are different. Our research finds that the length of the domain name has an impact on the performance of the DGA domain name detection model. In other words, the model is sensitive to the length of the domain name. In this case, attackers can evade detection simply by designing domain names of specific lengths. Moreover, the detection accuracy of DGA domain names still needs to be further improved. To solve these problems, three feature extraction methods adapted to the length of the domain name are proposed in this paper. For extra-short domain names, we use the attention-based method to extract features, which can make use of the character-level semantic feature. For moderate-length domain names, a two-dimensional structure, namely Right Shifted Tensor (RST), is constructed to make the domain name present apparent features similar to images. For the extra-long domain name, the effective classification of domain names can be achieved by manually crafted easy-to-calculate features. Then, different detection structures are designed based on these tree feature extraction methods to form a heterogeneous DGA detection model, namely HAGDetector. In addition, the public suffix is an important part of the domain name. We further analyze the public suffix to evaluate its impact on the detection of DGA domain names. Finally, the experiments are conducted to assess the validity of HAGDetector, as well as compare our approach with the current state-of-the-art and highlight the impact of the domain name length. The experimental results show that our method greatly improves the detection performance.