Deep Adversarial Architectures for Detecting and Generating Maliciousness

Explore deep adversarial architectures for detecting and generating malicious content in this 39-minute conference talk from BSidesLV 2016. Dive into the world of cybersecurity as Hyrum Anderson presents an overview of deep learning techniques applied to malware detection and generation. Learn about logistic regression, deep learning packages, and the key vulnerabilities in deep learning models. Discover the concepts of red team modeling, autoencoders, and the comparison between shallow and deep learning approaches. Gain insights into hardening techniques, false positives, and vulnerability assessment. Engage with topics such as Deep DJ, Deep TJ, and Deep DGA, and understand their implications for real-world cybersecurity applications.