School of Information Studies Research Seminar Series: Machine Learning for Cybersecurity and Neuroscience

This talk is free and open to all

Dr. Benjamin Fung

Professor, School Of Information Studies, McGill University

Canada Research Chair in Data Mining for Cybersecurity

Abstract:

In this talk, Prof. Fung will discuss two research problems in authorship and malware analyses and a new project in neuroscience. (1) Given an anonymous e-mail or some tweets, can we identify the author or infer the author's characteristics based on his/her writing styles? A representation learning method for authorship analysis and a live software demonstration will be presented. (2) 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. Prof. Fung’s award-winning assembly clone search engine will be described. (3) His steam has recently started a new collaborative research project with a neuroscience team at McGill to study stress and memory seeking to address an interesting research problem that will be discussed. The potential of machine learning will be examined in that context. If time permits, the talk will cover the new online cybersecurity certificate offered by the School of Information Studies.

Bio:

Dr. Benjamin Fung is a Canada Research Chair in Data Mining for Cybersecurity, a Full Professor of 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 140 refereed articles that span across the research forums of data mining, machine learning, privacy protection, and cybersecurity with over 13,000 citations. A report from Stanford University identified him to be among the top 2% scientists worldwide. His data mining works in crime investigation and authorship analysis have been reported by media, including New York Times, BBC, CBC, etc. Dr. Fung is an Associate Member of MILA and a licensed professional engineer in software engineering.

See his research website http://dmas.lab.mcgill.ca/fung for more information.