DEF CON 26 AI VILLAGE - infosecanon - The Current State of Adversarial Machine Learning

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Machine learning is quickly becoming a ubiquitous technology in the computer security space, but how secure is it exactly? This talk covers the research occurring in adversarial machine learning and includes a discussion of machine learning blind spots, adversarial examples and how they are generated, and current blackbox testing techniques.

Heather Lawrence is a cyber data scientist working with NARI. She earned her undergraduate and MS degrees in Computer Engineering from the University of Central Florida focusing on computer security. She is pursuing a PhD in Computer Engineering from the University of Nebraska Lincoln. Her previous experience in cyber threat intelligence modeling, darknet marketplace research, IT/OT testbed development, data mining, and machine learning has led to several awards from capture-the-flag competitions including the National Collegiate Cyber Defense Competition, CSI CyberSEED, and SANS Netwars Tournament. Her current research interests focus on the application of machine learning to cybersecurity problem sets.

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DEF CON 26 AI VILLAGE - infosecanon - The Current State of Adversarial Machine Learning DEF CON 26 AI VILLAGE -  infosecanon  - The Current State of Adversarial Machine Learning Reviewed by Dump3R H3id3gg3R on November 28, 2018 Rating: 5