{"id":24173,"date":"2018-11-29T06:51:34","date_gmt":"2018-11-29T06:51:34","guid":{"rendered":"https:\/\/www.eccu.edu\/?p=24173"},"modified":"2023-06-05T07:31:00","modified_gmt":"2023-06-05T07:31:00","slug":"how-can-we-work-towards-security-in-machine-learning","status":"publish","type":"post","link":"https:\/\/www.eccouncil.org\/cybersecurity-exchange\/cyber-talks\/how-can-we-work-towards-security-in-machine-learning\/","title":{"rendered":"How Can We Work Toward Security in Machine Learning?"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

\"Dr.<\/p>\n

Name:<\/strong> Dr. Catherine Huang
\nTopic:<\/strong> How can we work towards security in machine learning
\nDate of Webinar:<\/strong> 21st<\/sup> March, 2019
\nTime and Location:<\/strong> 9pm PST \/ 9:30am IST\/ 6:00am GMT<\/p>\n

Watch Now<\/a><\/p>\n


<\/strong><\/p>


<\/strong><\/p>

Speaker Bio:<\/strong> Dr. Catherine Huang is a Principal Engineer in McAfee LLC. Her expertise is adversarial machine learning, deep learning and artificial intelligence for security applications. Catherine is currently the chair of IEEE Cognitive and Developmental Systems Technical Committee and a member of IEEE Neural Networks Technical Committee and IEEE Machine Learning for Signal Processing Technical Committee. Previously, Catherine was a Senior Research Scientist at Security & Privacy Research in Intel Labs. She directed ML research at the Intel Science and Technology Center for Security Computing in University of California Berkeley from 2015 to 2016. She received her Ph.D. in Brain Computer Interfaces in USA in 2010 and M.S. in CSEE in Canada in 2005. She has 7 US patents, 37 book chapters and papers with over 1400 citations. She gave a keynote on Challenges and Opportunities in Cybersecurity Intelligence at IEEE Symposium Series on Computation Intelligence in 2016.<\/p>\n

Topic Abstract:<\/strong> In recent years, machine learning (ML) has become a powerful tool to overcome long-standing computational problems. ML has pushed the boundaries of state-of-the art image recognition, speech recognition, and has become effective in solving a wide-range of problems, such as natural language processing, AlphaGo and autonomous driving. As such, ML holds massive potential to drive human progress. As the presence of ML increases, so too does its likelihood of being attacked, hijacked, or manipulated. In this talk, we will examine the vulnerabilities of ML systems, as well as potential solutions. We hope to motivate better use and safe practices of ML for security applications.<\/p>\n

*Examples, analysis, views and opinion shared by the speakers are personal and not endorsed by EC-Council or their respective employer(s)<\/p>\n

<\/a>
\n