Presented at
BSidesSF 2022 Rescheduled,
June 5, 2022, 2 p.m.
(50 minutes)
Machine learning, while being one of the most hyped and anticipated technology paradigm shifts, has yet to be widely applied to threat hunting and detection. This talk covers two years of work on machine learning models for threat detection. Case studies will include numerous high-value detections.
Presenters:
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Craig Chamberlain
- Elastic
Craig has seen things you people wouldn't believe. Attack ships on fire off the shoulder of Orion, C-beams glittering in the dark near the Tannhäuser Gate. Craig is a longtime security researcher who has been to the places and done the kinds of things you would expect, most of which cannot be discussed here. He has twice served as a chief security architect and was a principal at several successful security product startups. He is currently serving as a detection science area lead, and part-time festival organizer, at a large security research and product development organization.
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Omid Mirzaei
- Elastic
Omid Mirzaei is a senior security data scientist on the protections team at Elastic. He develops machine learning tools for the cybersecurity domain and does research on how to build trustworthy ML-based systems. His research interests include computer security, mobile security, malware analysis and applied machine learning in security. He did his PhD in COmputer SECurity (COSEC) lab at University Carlos III of Madrid (UC3M), Spain. During his PhD, he worked on Android application triage, malware detection, and characterization. Before joining Elastic, he was a postdoctoral research associate in the Systems Security Lab (SecLab) and a Part-Time lecturer at Northeastern University in Boston. During this period, he conducted extensive research on detecting code reuse in advanced malware being used by different campaigns in targeted attacks. He also has taught cybersecurity courses at both undergraduate and graduate levels.
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