ProvADShield: A Multimodel Ensemble Defender Against Adversarial Attacks on Provenance Graph Host Intrusion Detector

HID (host intrusion detection) is a security mechanism for detecting malicious activities performed in a host (e.g., a server, an edge device). Recent research has recast HID as a provenance graph learning problem thanks to the advancement in deep learning techniques, especially the GNNs (graph neural networks). Although the provenance graph learning based HID methods show promise, they are vulnerable to adversarial attacks, where the attackers can bypass the HID models by carefully modifying their attack behaviors. In this paper, we reveal that an adversarial sample generated against one HID model may not be necessarily able to attack another HID model, and we further explore the success rate of adversarial attacks between different HID models by evaluating the mutual transferability. Based on the evaluation, we propose ProvADShield, a framework designed to defend against adversarial attacks on provenance graph learning based HID models. The core idea of ProvADShield is to combine multiple HID models by leveraging the mutual transferability. We evaluate ProvADShield based on a provenance dataset collected and made public by our team. The experiment results show that ProvADShield outperforms state‐of‐the‐art defense systems against adversarial attacks.