The 2024 INTERNATIONAL workshop on
Artificial Intelligence & Cyber Security
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Deep learning has changed our society as the most powerful AI tool. However, it is used without theoretical backgrounds since many techniques in deep learning are heuristics. This talk introduces some of the theoretical works on the effects of such techniques to the performance of deep learning.
Kazushi Ikeda got his B.E., M.E., and Ph.D. in Mathematical Engineering from University of Tokyo in 1989, 1991, and 1994. He joined Kanazawa University as an assistant professor in 1994 and became a junior/senior associate professor of Kyoto University in 1998 and 2003, respectively. He has been a full professor of NAIST since 2008.
As organisations increasingly adopt generative AI chatbots to navigate complex legal and policy driven environments—such as payroll, tax regulations, employment legislation, union agreements, employment contracts, and company policies—cybersecurity becomes a critical concern. This talk delves into the intersection of cybersecurity and AI within enterprise environments where chatbots and agentic systems provide assistance on sensitive and regulated information.
Richard Kenyon is the Associate Director of Datapay AI Labs. During his 25 years working in Software Engineering the power of automation has always been top of mind for releasing ‘latent capacity’ within teams. He started his career (back in the 1990s) doing post graduate research into the application of Neural Networks to Multivariate Data with application in the Biotechnology Industry for optimising Penicillin fermentations before moving into industry and has been following the advances in AI and ML ever since. He has worked on various ML based systems for analysing unstructured content including content secured in Government document management systems and is excited about the potential of GenAI and ML to build Enterprise Business Applications with Natural Language interfaces.
With the advances in Machine Learning techniques, extracting actionable intelligence from large data sets have changed many technologies. Concepts such as Industry 4.0, Threat Intelligence, Autonomous Vehicles are now commonplace with Machine Learning being a prime driver. In this talk, we discuss how Cyber Security — a necessary technology in today’s digitalized world has also been impacted by Machine Learning. In particular, we discuss two specific areas — malware analysis, and intrusion detection where we have been using machine learning to provide effective security to systems and networks. We also discuss briefly, how machine learning processes – i.e training, and classification/regression etc., are also being attacked by cyber attackers, and how machine learning techniques are evolving through these threats through adversarial machine learning.
Prof. Sandeep K. Shukla is an IEEE fellow, and ACM Distinguished Scientist. He is currently a professor of Computer Science and Engineering department at IIT Kanpur which he headed during 2017-2020. He was the editor in chief of the ACM Transactions on Embedded Computing Systems during 2013-2020. He is currently associate editors of ACM Transactions on Cyber Physical Systems, and Journal of the British Blockchain Association. In the past he has served as associate editors of IEEE Transactions on Computers, IEEE Transactions on Industrial Informatics, IEEE Design and Test, and IEEE Embedded Systems Letters. Before joining IIT Kanpur in 2015, he was a professor at Virginia Tech, USA. He served as ACM Distinguished Speaker, and IEEE Computer Society Distinguished Visitor in the past. He has authored over 200 peer reviewed journal and conference papers, and authored/edited 10 books. He was awarded the Presidential Early Career Award in Science and Engineering (PECASE) in 2004, The Bessel Award by Humboldt Foundation in 2009, a Distinguished Alumnus Award by SUNY Albany in 2007, a Ramanujan Fellowship in 2015. His major research interest is Cyber Security of Critical Infrastructures, Cyber Security of IT/OT systems, and Applications of Blockchain Technology in Security and Privacy.
To be announced soon.