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    研究生学术年会系列报告--Networked Systems and Security Research in the Age of AI/Machine Learning
    日期:2022-09-07 作者:张艳 浏览次数:

    Title: Networked Systems and Security Research in the Age of AI/Machine Learning

    Abstract: Networked systems have created new opportunities with major societal implications. At the same time, security has emerged as one of the most important socio-technical challenges confronting society. AI/machine learning (ML) techniques are expected to enable networked systems and enhance security. In this talk, I will present my recent research on networked systems and security in the age of AI/ML. First, I will introduce my ML-enabled Counter Unmanned Aircraft System(s) (C-UAS) technology that detects and safely neutralizes rogue drones without destroying them or causing them to crash. This research has been featured by 100+ news media outlets. Next I will present my follow-up research on real-time ML for quickest event (threat/intrusion/vulnerability…) detection. Then I will introduce my research on data-efficient ML, particularly distant domain transfer learning.

    Speaker: Houbing Song is with the Security and Optimization for Networked Globe Laboratory (SONG Lab), Embry-Riddle Aeronautical University, Daytona Beach, FL 32114 USA (email: h.song@ieee.org)


    Short Bio:

    Houbing Song (M’12–SM’14) received the Ph.D. degree in electrical engineering from the University of Virginia, Charlottesville, VA, in August 2012.

    Tenured Associate Professor , the Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, where he is currently the Director of the Security and Optimization for Networked Globe Laboratory (SONG Lab, www.SONGLab.us). His research interests include cyber-physical systems/internet of things, cybersecurity and privacy, AI/machine learning/big data analytics, edge computing, unmanned aircraft systems, connected vehicle, smart and connected health, and wireless communications and networking. Dr. Song is a senior member of IEEE and ACM, and an ACM Distinguished Speaker. Dr. Song is a Highly Cited Researcher identified by Clarivate™ (2021) and a Top 1000 Computer Scientist identified by Research.com.