Applied Machine Learning: Defeating Modern Malicious Documents


Deep dive into analysis of malicious code embedded in Microsoft Office documents. This session will analyze this insidious attack vector through machine learning techniques.  Attendees will be introduced to machine learning concepts and how they can be applied to enhance detection and derive new threat intelligence.


Evan Gaustad manages a threat detection team at the Target Corporation’s Cyber Fusion Center (CFC). Evan 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 Interactive Intelligence 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.