POIROT: Aligning Attack Behavior with Kernel Audit Records for Cyber Threat Hunting

TL;DR

POIROT is a novel system that leverages kernel audit logs and CTI correlations to effectively identify attack campaigns across multiple OS platforms through graph pattern matching.

Contribution

It introduces a new similarity metric for aligning CTI-based query graphs with kernel audit provenance graphs for threat hunting.

Findings

POIROT can analyze graphs with millions of nodes within minutes.

It successfully identified attack campaigns in real-world and DARPA-designed scenarios.

CTI correlations are effective artifacts for threat detection.

Abstract

Cyber threat intelligence (CTI) is being used to search for indicators of attacks that might have compromised an enterprise network for a long time without being discovered. To have a more effective analysis, CTI open standards have incorporated descriptive relationships showing how the indicators or observables are related to each other. However, these relationships are either completely overlooked in information gathering or not used for threat hunting. In this paper, we propose a system, called POIROT, which uses these correlations to uncover the steps of a successful attack campaign. We use kernel audits as a reliable source that covers all causal relations and information flows among system entities and model threat hunting as an inexact graph pattern matching problem. Our technical approach is based on a novel similarity metric which assesses an alignment between a query graph constructed out of CTI correlations and a provenance graph constructed out of kernel audit log records. We evaluate POIROT on publicly released real-world incident reports as well as reports of an adversarial engagement designed by DARPA, including ten distinct attack campaigns against different OS platforms such as Linux, FreeBSD, and Windows. Our evaluation results show that POIROT is capable of searching inside graphs containing millions of nodes and pinpoint the attacks in a few minutes, and the results serve to illustrate that CTI correlations could be used as robust and reliable artifacts for threat hunting.