Abstract: Cyberattacks are malicious acts that aim to steal and damage data or disrupt the digital life, which are arguably one of the most serious threats we are facing in today's world. It is very challenging to detect such cyberattacks because most of them are very stealthy and sophisticated and cannot be captured by traditional rule-based defenses. In this talk, I will explore the opportunities to develop the defenses by leveraging machine learning algorithms. Particularly, I will use real-life scenarios to show how machine learning can provide us with the technology to augment the defense capabilities against cyberattacks. Meanwhile, I will talk about the challenges for machine learning that we have yet to overcome, which may significantly impact the effectiveness of machine learning empowered defenses.
Bio: Qi Li received his Ph.D. degree from Tsinghua University. Now he is an associate professor of Institute for Network Sciences and Cyberspace, Tsinghua University. He has ever worked at ETH Zurich and the University of Texas at San Antonio. His research interests are in network and system security, particularly in leveraging machine learning to enhance Internet security, mobile security, and big data security. He is currently an editorial board member of IEEE TDSC and ACM DTRAP, and serves on the organization or program committees of various premier conferences, e.g., IEEE S&P, USENIX Security, ACM CCS, and ISOC NDSS.
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