Malware Meets Machine Learning

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Machine learning has proven to be helpful in many areas in cyber security, e.g., malware detection, intrusion detection, and fuzzing. The ability of machine learning models to identify patterns allows it to be a valuable tool that can be used to increase efficiency and efficacy while also decreasing costs. This becomes especially important as malicious actors continue to develop more sophisticated attacks against a growing attack surface. However, more research must be done before machine learning can be safely integrated into cyber defense as machine learning models themselves are not secure. In this talk, I will be presenting an attack against malware detectors using adversarial machine learning and a defense against such adversaries. 



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  • Date: 07 Dec 2021
  • Time: 12:00 PM to 12:45 PM
  • All times are US/Eastern
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  Speakers

Daniel Park

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Dr. Daniel Park Invited Speaker

Daniel Park is a research computer scientist at the Information Directorate, Air Force Research Laboratory. He earned his PhD in 2021 at Rensselaer Polytechnic Institute working with the RPI Security Lab. His research mainly focuses on the security of machine learning models. He has also led various RPI-Seclab projects on binary lifting, fuzzing, and malware analysis.