From False Positives to Actionable Analysis

Explore an innovative approach to modeling human behavior in network traffic for automatic labeling of security-relevant events in this 45-minute Black Hat conference talk. Learn how to create a scalable framework of distributed forensic artificial intelligences to hunt for malicious activities and streamline remediation processes. Discover techniques for building a next-generation cybersecurity analytics engine that reduces false positives, attributes attacks to threat actors, and minimizes detection time. Gain insights into leveraging machine learning to develop dynamic models that adapt to evolving threats and identify previously unseen vulnerabilities. Understand how to design and implement a central nervous system for Security Operations Centers (SOCs) using existing SIEM and IT warehouse data. Acquire knowledge on roadmapping cybersecurity analytics and calculating optimal return on investment based on current coverage and threat surface mapping.

From False Positives To Actionable Analysis