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Applied Machine Learning: Defeating Modern Malicious Documents (RSA Conference 2017)
A common tactic adopted by attackers for initial exploitation is the use of malicious code embedded in Microsoft Office documents. This attack vector is not new, but attackers are still having success. This session will dive into the details of these techniques, introduce some machine learning approaches to analyze and detect these attempts, and explore the output in Elasticsearch and Kibana.
Evan Gaustad manages a threat detection team at the Target Corporation's Cyber Fusion Center (CFC). In the CFC Gaustad and his team analyze threat intelligence, build detection in a variety of SIEM tools, and develop innovative custom detection tools and infrastructure. He has over a decade of experience in security working in various roles from system security engineering to penetration testing in defense, banking, and retail industries. He is currently in the Georgia Tech OMSCS master’s program for machine learning and holds an M.S. in information security technology from Carnegie Mellon University, an MBA from St. Thomas University, and a B.S. in computer science from the University of Minnesota.
Detailed Presentation :
(Source: RSA USA 2017)
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