RMD-Graph: Adversarial Attacks Resisting Malicious Domain Detection Based on Dual Denoising

Abstract

The Domain Name System (DNS) is a critical Internet service that translates domain names into IPs, but it is often targeted by attackers, posing a serious security risk. Graph-based models for detecting malicious domains have shown high performance but are vulnerable to adversarial attacks. To address this issue, we propose RMD-Graph, which is characterized by its ability to resist adversarial attacks and its low dependency on labeled data. A dual denoising module is specifically designed based on two autoencoders to generate the reconstructed graph, where SVD, TOP-k and reconstruction loss are introduced to enhance the denoising capability of autoencoders. Subsequently, residual connections are employed to generate an optimized graph that retains essential information from the original graph. The reconstructed graph and the optimized graph are then utilized as two views for graph contrastive learning, thereby achieving an self-supervised representation learning task without labels. In the downstream malicious domain detection, the denoised node representations are employed for machine learning classification. Extensive experiments are conducted on publicly available DNS datasets, and the results demonstrate that RMD-Graph significantly outperforms known baseline methods, especially in adversarial scenarios.