threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning

Abstract

Host-based threats such as Program Attack, Malware Implantation, and Advanced Persistent Threats (APT), are commonly adopted by modern attackers. Recent studies propose leveraging the rich contextual information in data provenance to detect threats in a host. Data provenance is a directed acyclic graph constructed from system audit data. Nodes in a provenance graph represent system entities (e.g., $processes$ and $files$) and edges represent system calls in the direction of information flow. However, previous studies, which extract features of the whole provenance graph, are not sensitive to the small number of threat-related entities and thus result in low performance when hunting stealthy threats. We present threaTrace, an anomaly-based detector that detects host-based threats at system entity level without prior knowledge of attack patterns. We tailor GraphSAGE, an inductive graph neural network, to learn every benign entity's role in a provenance graph. threaTrace is a real-time system, which is scalable of monitoring a long-term running host and capable of detecting host-based intrusion in their early phase. We evaluate threaTrace on three public datasets. The results show that threaTrace outperforms three state-of-the-art host intrusion detection systems.

arXiv comment13 pages, 6 figures

References in corpus (9)

• Adversarial Attacks on Neural Networks for Graph Data
• UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats
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• POIROT: Aligning Attack Behavior with Kernel Audit Records for Cyber Threat Hunting
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Cited by in corpus (2)

• TREC: APT Tactic / Technique Recognition via Few-Shot Provenance Subgraph Learning
• Defending Network Intrusion Detection Systems Based on Graph Neural Networks Against Structural Adversarial Attacks