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 styles. The third problem is on 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.

Bio: Prof. 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 14,000 citations and h-index 57. 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 a licensed professional engineer in software engineering. See his research website http://dmas.lab.mcgill.ca/fung for more information.

Abstract : Signals are the means by which information is transmitted from one point to another, whether it is sound/radio waves, speech, images, or even biological signals like an ECG. Understanding and analyzing signals are vital because they provide insights into their underlying information, enabling accurate communication, data interpretation, and decision-making across various domains, from telecommunications to healthcare. Generative Artificial Intelligence (AI) holds the potential to revolutionize the analysis, manipulation, and interpretation of signals by autonomously generating high-quality data, optimizing signal features in real-time, and providing innovative solutions for complex signal processing challenges, paving the way for advanced and efficient data-driven applications. This talk delves into the world of Generative AI and its transformative potential in signal processing and information security. Through real-world examples and case studies, we showcase the tangible benefits and opportunities that Generative AI brings to organizations across various industries.

Bio : Prof. Dr. Amin Beheshti is a Full Professor of Data Science, the Director of the Centre for Applied Artificial Intelligence, the head of the Data Science Lab, and the founder of the Big Data Society at Macquarie University, Sydney, Australia. Additionally, he is an Adjunct Professor of Computer Science at UNSW Sydney, Australia. Amin completed his PhD and Postdoc in Computer Science and Engineering at UNSW Sydney, and holds a Master's and Bachelor's degree in Computer Science, both with First Class Honours. Alongside his teaching activities, Amin has made significant contributions to research projects, serving as the R&D Team Lead and Key Researcher in the 'Case Walls & Data Curation Foundry' and 'Big Data for Intelligence' projects. These projects were awarded the National Security Impact Award in both 2016 and 2017. As a distinguished researcher in Big-Data/Data/Process Analytics, Amin has been invited to serve as a Keynote Speaker, General-Chair, PC-Chair, Organisation-Chair, and program committee member of top international conferences. He is also a leading author of several authored books in data, social, and process analytics, co-authored with other high-profile researchers. Amin has secured over $23.9 million in research grants for AI-Enabled, Data-Centric, and Intelligence-Led projects.