APT-CGSV: APT Detection via Contrastive GNNs and Semantic Validation

Abstract

Advanced Persistent Threats (APTs) increasingly employ “Living-off-the-Land” techniques, rendering traditional signature-based detection ineffective. While query-based threat hunting using system provenance graphs provides essential behavioral context, existing approaches, specifically Graph Neural Network (GNN) based methods, can suffer from a critical vulnerability to structural mimicry, where benign administrative tasks topologically resemble malicious campaigns. Large Language Models (LLMs) offer the deep semantic reasoning required to resolve this ambiguity, but applying them to raw enterprise logs is constrained by prohibitive latency, limited context, and data privacy constraints. In this paper, we propose a novel neuro-symbolic framework that bridges this gap via a highly efficient two-stage cascade architecture. First, a self-supervised contrastive Graph Isomorphism Network with Edge features (GINE) acts as a high-recall structural filter. Trained on a curated dataset of just 24,000 subgraphs, which is much smaller than those used by existing supervised learning-based approaches, our encoder learns robust representations resilient to graph noise and successfully eliminates 87% to over 99% of the benign structural mimics in our curated test sets. Second, the reduced candidate set is processed by a localized LLM utilizing deterministic artifact matching and constrained Chain-of-Thought (CoT) behavioral matching for semantic reasoning. Evaluated across diverse operating systems (Windows, Linux, FreeBSD) using our curated test sets from the DARPA TC3 and OpTC datasets, our framework achieves superior precision and F1-scores as compared to existing baseline systems. By combining the data efficiency of contrastive GNNs with the semantic depth of localized LLMs, we provide a scalable, highly accurate, and efficient threat-hunting solution.