SAGE: Intrusion Alert-driven Attack Graph Extractor

TL;DR

SAGE is an unsupervised system that automatically extracts attack graphs from intrusion alerts, capturing attacker strategies without prior expert knowledge, thus enabling more efficient and interpretable cybersecurity analysis.

Contribution

It introduces SAGE, a novel unsupervised sequence learning method that derives attack graphs from alerts, reducing reliance on costly vulnerability scans and expert input.

Findings

Successfully compressed 330k alerts into 93 attack graphs

Captured attacker strategies and behavioral dynamics

Produced succinct, interpretable attack graphs

Abstract

Attack graphs (AG) are used to assess pathways availed by cyber adversaries to penetrate a network. State-of-the-art approaches for AG generation focus mostly on deriving dependencies between system vulnerabilities based on network scans and expert knowledge. In real-world operations however, it is costly and ineffective to rely on constant vulnerability scanning and expert-crafted AGs. We propose to automatically learn AGs based on actions observed through intrusion alerts, without prior expert knowledge. Specifically, we develop an unsupervised sequence learning system, SAGE, that leverages the temporal and probabilistic dependence between alerts in a suffix-based probabilistic deterministic finite automaton (S-PDFA) -- a model that accentuates infrequent severe alerts and summarizes paths leading to them. AGs are then derived from the S-PDFA on a per-objective, per-victim basis. Tested with intrusion alerts collected through Collegiate Penetration Testing Competition, SAGE compresses over 330k alerts into 93 AGs. These AGs reflect the strategies used by the participating teams. The AGs are succinct, interpretable, and capture behavioral dynamics, e.g., that attackers will often follow shorter paths to re-exploit objectives.