12/9開催 第10回特別講演会〝Data Mining and Machine Learning for Authorship and Malware Analyses”
innovative Photonics Evolution Research Center 10th Special Seminar
“Data Mining and Machine Learning for Authorship and Malware Analyses”
Dr. Benjamin C. M. Fung
Canada Research Chair & Associate Professor, School of Information Studies, McGill University, Canada
Date：4:00pm-5:00pm, December 9(Mon), 2019
Location： Conference Room 420, innovative Photonics Evolution Research Center 4F
In this talk, we 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? I will present a representation learning method for authorship analysis and give a live software demonstration. (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. We have implemented an award-winning assembly clone search engine called Kam1n0. It is the first clone search engine that can efficiently identify a given query assembly function’s subgraph clones from a large assembly code repository. I will give a live demonstration of Kam1n0. (3) Our team has recently started a new collaborative research project with a neuroscience team at McGill to study stress and memory. We do not have any significant result yet, but the research problem itself is interesting. I would like to share it with the goal of initiating students’ interests in machine learning.
Dr. Benjamin Fung is a Canada Research Chair in Data Mining for Cybersecurity, an Associate Professor of School of Information Studies (SIS) at McGill University, a Co-curator of Cybersecurity in the World Economic Forum (WEF), and an Associate Editor of IEEE Transactions on Knowledge and Data Engineering (TKDE). 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 120 refereed articles that span across the research forums of data mining, machine learning, privacy protection, cyber forensics, services computing, and building engineering with over 8,900 citations. His data mining works in crime investigation and authorship analysis have been reported by media worldwide, 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.
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