DARKMENTION: A Deployed System to Predict Enterprise-Targeted External Cyberattacks

TL;DR

DARKMENTION is a system that predicts enterprise-targeted cyberattacks by analyzing darkweb forums, generating timely warnings that outperform baseline methods and are deployed in real-world settings, providing proactive security alerts.

Contribution

The paper introduces DARKMENTION, a novel system that automatically learns association rules from darkweb data to predict cyberattacks before they occur, improving warning accuracy and timeliness.

Findings

DARKMENTION outperforms baseline systems with 45-57% higher F1 scores.

It generates warnings on average 3 days before attacks.

The system is deployed in a real-world operational environment.

Abstract

Recent incidents of data breaches call for organizations to proactively identify cyber attacks on their systems. Darkweb/Deepweb (D2web) forums and marketplaces provide environments where hackers anonymously discuss existing vulnerabilities and commercialize malicious software to exploit those vulnerabilities. These platforms offer security practitioners a threat intelligence environment that allows to mine for patterns related to organization-targeted cyber attacks. In this paper, we describe a system (called DARKMENTION) that learns association rules correlating indicators of attacks from D2web to real-world cyber incidents. Using the learned rules, DARKMENTION generates and submits warnings to a Security Operations Center (SOC) prior to attacks. Our goal was to design a system that automatically generates enterprise-targeted warnings that are timely, actionable, accurate, and transparent. We show that DARKMENTION meets our goal. In particular, we show that it outperforms baseline systems that attempt to generate warnings of cyber attacks related to two enterprises with an average increase in F1 score of about 45% and 57%. Additionally, DARKMENTION was deployed as part of a larger system that is built under a contract with the IARPA Cyber-attack Automated Unconventional Sensor Environment (CAUSE) program. It is actively producing warnings that precede attacks by an average of 3 days.