APT-CGLP: Advanced Persistent Threat Hunting via Contrastive Graph-Language Pre-Training

TL;DR

APT-CGLP introduces a contrastive graph-language pre-training approach that enables end-to-end semantic matching between provenance graphs and CTI reports, significantly improving APT threat hunting accuracy and efficiency.

Contribution

It presents a novel end-to-end system leveraging LLMs and contrastive learning to align provenance graphs with CTI reports without manual graph extraction.

Findings

Outperforms state-of-the-art baselines in accuracy

Demonstrates effectiveness on four real-world datasets

Enhances threat hunting efficiency

Abstract

Provenance-based threat hunting identifies Advanced Persistent Threats (APTs) on endpoints by correlating attack patterns described in Cyber Threat Intelligence (CTI) with provenance graphs derived from system audit logs. A fundamental challenge in this paradigm lies in the modality gap -- the structural and semantic disconnect between provenance graphs and CTI reports. Prior work addresses this by framing threat hunting as a graph matching task: 1) extracting attack graphs from CTI reports, and 2) aligning them with provenance graphs. However, this pipeline incurs severe \textit{information loss} during graph extraction and demands intensive manual curation, undermining scalability and effectiveness. In this paper, we present APT-CGLP, a novel cross-modal APT hunting system via Contrastive Graph-Language Pre-training, facilitating end-to-end semantic matching between provenance graphs and CTI reports without human intervention. First, empowered by the Large Language Model (LLM), APT-CGLP mitigates data scarcity by synthesizing high-fidelity provenance graph-CTI report pairs, while simultaneously distilling actionable insights from noisy web-sourced CTIs to improve their operational utility. Second, APT-CGLP incorporates a tailored multi-objective training algorithm that synergizes contrastive learning with inter-modal masked modeling, promoting cross-modal attack semantic alignment at both coarse- and fine-grained levels. Extensive experiments on four real-world APT datasets demonstrate that APT-CGLP consistently outperforms state-of-the-art threat hunting baselines in terms of accuracy and efficiency.