DARKMENTION: A Deployed System to Predict Enterprise-Targeted External Cyberattacks
This addresses the need for organizations to proactively identify cyberattacks, though it is incremental as it builds on existing threat intelligence methods.
The paper tackles the problem of predicting enterprise-targeted cyberattacks by developing DARKMENTION, a system that learns association rules from darkweb/deepweb data to generate warnings, resulting in an average increase in F1 score of about 45% and 57% over baselines and warnings preceding attacks by an average of 3 days.
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.