Near Real-time Learning and Extraction of Attack Models from Intrusion Alerts
This addresses the challenge for SOC analysts who are inundated with alerts and need actionable intelligence, though it appears incremental as it builds on existing unsupervised learning approaches.
The paper tackles the problem of overwhelming intrusion alerts in Security Operation Centers by deploying ASSERT, an unsupervised learning system that extracts and updates attack models in near real-time from streaming alerts, generating a small number of statistical models to help analysts comprehend attacks.
Critical and sophisticated cyberattacks often take multitudes of reconnaissance, exploitations, and obfuscation techniques to penetrate through well protected enterprise networks. The discovery and detection of attacks, though needing continuous efforts, is no longer sufficient. Security Operation Center (SOC) analysts are overwhelmed by the significant volume of intrusion alerts without being able to extract actionable intelligence. Recognizing this challenge, this paper describes the advances and findings through deploying ASSERT to process intrusion alerts from OmniSOC in collaboration with the Center for Applied Cybersecurity Research (CACR) at Indiana University. ASSERT utilizes information theoretic unsupervised learning to extract and update `attack models' in near real-time without expert knowledge. It consumes streaming intrusion alerts and generates a small number of statistical models for SOC analysts to comprehend ongoing and emerging attacks in a timely manner. This paper presents the architecture and key processes of ASSERT and discusses a few real-world attack models to highlight the use-cases that benefit SOC operations. The research team is developing a light-weight containerized ASSERT that will be shared through a public repository to help the community combat the overwhelming intrusion alerts.