Towards Taming Adversarial Machine Learning: Security Applications PerspectivesSpeaker: David Mohaisen – Orlando, FL, United States
Topic(s): Security and Privacy
AbstractThe recent rapid advances in machine and deep learning algorithms have found many applications in the security space, targeting various applications including intrusion detection systems, malware detection, and attribution. Despite their extraordinary superhuman performance in various tasks, machine learning algorithms are prone to adversarial examples, carefully crafted input examples to the machine algorithms that will result in fooling the machine algorithms by, for example, reducing their confidence or even resulting in misclassification. In this talk, we review advances in adversarial machine learning space as it pertain to various application security tasks. We further highlight and review several recent studies to demonstrate the success of adversarial examples on various applications, including website fingerprinting, malicious binaries classification, source code authorship identification, and intrusion detection systems. We discuss various defenses and conclude with open directions.
About this LectureNumber of Slides: 50
Duration: 60 minutes
Languages Available: English
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