Time: 16: 15 – 17: 45 (JST), July 21, 2023.

Location: R205, Education & Research Building No. 8, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran, Hokkaido, 0508585 Japan.

CO-ORGANIZED BY:

IEEE Muroran Institute of Technology Student Branch

IEEE Sapporo Section Young Professionals (YP)

Emerging Networks and Systems Laboratory (ENeS), Muroran Institute of Technology

Topic: Machine Learning for Cybersecurity and Privacy

Speaker: Benjamin C. M. Fung, Canada Research Chair & Professor, School of Information Studies, McGill University, Canada

Abstract:

Three research directions in cybersecurity and privacy will be presented in this session. The first research direction is on privacy-preserving data publishing. The objective is to share large volumes of data for machine learning without compromising the privacy of individuals. We will discuss multiple data-sharing scenarios in privacy-preserving data publishing. The second research direction is on authorship analysis. The objective is to identify the author or infer the author’s characteristics based on his/her writing style. The third problem is malware analysis. Assembly code analysis is one of the critical processes for mitigating the exponentially increasing threats from malicious software. However, it is a manually intensive and time-consuming process even for experienced reverse engineers. An effective and efficient assembly code clone search engine can greatly reduce the effort of this process. I will briefly describe our award-winning assembly clone search engine.

Biography:

Professor Benjamin Fung is a Canada Research Chair in Data Mining for Cybersecurity, a Full Professor of the School of Information Studies (SIS) at McGill University, and an Associate Editor of IEEE Transactions on Data and Engineering (TKDE) and Elsevier Sustainable Cities and Society (SCS). He received a Ph.D. degree in computing science from Simon Fraser University in 2007. Collaborating closely with the national defense, law enforcement, transportation, and healthcare sectors, he has published over 150 refereed articles that span across the research forums of data mining, machine learning, privacy protection, and cybersecurity with over 14,000 citations. His data mining works in crime investigation and authorship analysis have been reported by media, including New York Times, BBC, CBC, etc. Benjamin is a licensed professional engineer in software engineering. See his research website http://dmas.lab.mcgill.ca/fung for more information.

Contact:

Emerging Networks and Systems Laboratory (ENeS)

Email: [email protected]