2024-10-19 –, ENG 103
In the complex landscape of modern cybersecurity, identifying coordinated attacks within massive volumes of security data is a formidable challenge. Security professionals often grapple with distinguishing these attacks from numerous false positives and isolated incidents. This talk will illuminate how data science can be harnessed to transform tons of events, logs, and alerts into a bunch of clusters, a few kill chains, and fewer actionable insights, with open-source models.
Join us on a journey to enhance application security & security operations efficacy and efficiency
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Ezz Tahoun, a distinguished cyber-security data scientist, who won AI & innovation awards at Yale, Princeton and Northwestern. He also got innovation awards from Canada’s Communications Security Establishment, Microsoft US, Trustwave US, PIA US, NATO, and more. He ran data science innovation programs and projects for OrangeCyber Defense, Forescout Technologies, Royal bank of Canada, Governments, and Huawei Technologies US. He has published 20 papers, countless articles and 15 open source projects in the domain. When he was 19 years old he started his CS PhD in one of the top 5 labs in the world for cyber & AI, in the prestigious University of Waterloo, where he published numerous papers and became a reviewer for top conferences. His designations include: SANS/GIAC-Advisory-Board, aCCISO, CISM, CRISC, GCIH, GFACT, GSEC, CEH, GCP-Professional-Cloud-Architect, PMP, BENG and MMATH. He was an adjunct professor of cyber defense and warfare at Toronto’s school of management.