Multi-view adversarial attack defending method for host intrusion detection - WANG Fei; QIAN Kehan; LYU Mingqi; ZHU Tiantian; CHEN Honglong (Tongxin xuebao)
Host-based intrusion detection (HID) aims to identify attack behaviors through the analysis of host logs. In recent years, to address increasingly sophisticated host attacks, provenance graphs were leveraged to parse kernel audit logs, and graph neural network (GNN) were employed to train detection models, significantly enhancing detection performance. However, the inherent limitations of GNNs render these models vulnerable to adversarial evasion attacks. To mitigate this vulnerability, a multi-view adversarial attack defense method for host-based intrusion detection was proposed. The fundamental principle of this method was predicated upon three tenets. Firstly, structural and behavioural views were constructed through the fusion of multi-dimensional features, thereby circumventing the limitations imposed by a single perspective. Secondly, the transferability of adversarial samples between models was quantified, with complementary pairs of models exhibiting minimal migration subsequently being filtered. Thirdly, a hierarchical voting mechanism was conceptualized to integrate the decision outcomes of heterogeneous models, enhancing the system's robustness. The efficacy of the proposed method was evaluated using authentic host kernel log datasets. The experimental results demonstrate that the method exhibits superior performance compared to existing adversarial attack defense methods. Specifically, a malicious node recall rate exceeding 80% is achieved under typical adversarial attacks, representing a 23% increase over existing single-model defense methods. Additionally, the false alarm rate is maintained below 10%, substantiating the efficacy of the migration analysis-based fusion strategy for robustness enhancement