Keynote Title: AI in Cybersecurity: Attribution, Inference, and the Attack–Defense Arms Race
Abstract:
The rapid growth of artificial intelligence has transformed modeling, learning, and inference across domains. In cybersecurity, AI brings both power and risk. This keynote examines AI’s dual impact – enhancing defense through authorship attribution, LLM-based vulnerability analysis, automated repair, and self-adaptive networks, while introducing new threats such as privacy leaks in AR/VR systems. Drawing on empirical and system-level studies, it highlights how AI is reshaping the balance in the attack-defense race. The talk concludes with open challenges in robustness, interpretability, and trust toward designing secure, AI-driven systems.
Biography:
Aziz Mohaisen is a Professor of Computer Science at the University of Central Florida and Director of the Security and Analytics Lab (SEAL). He obtained his Ph.D. in Computer Science from the University of Minnesota in 2012. His research spans systems and network security, adversarial machine learning, malware analysis, blockchain, and trusted computing, with publications in leading venues such as IEEE S&P, USENIX Security, CCS, NDSS, HPCA, CHI, and ICCV. He previously held positions at Verisign Labs, the University at Buffalo (SUNY), and ETRI. His research has been supported by NSF, NRF, AFRL, and industry partners, and recognized with multiple university-wide honors, including the Faculty Excellence in Mentoring Doctoral Students Award, Research Incentive Award, Teaching Incentive Award, Excellence in Graduate Teaching Award, and FCI Faculty Fellowship. Having graduated 23 doctoral students, he is deeply committed to graduate education and mentorship. He serves on editorial boards for IEEE TDSC, TMC, TPDS, and TCC, and regularly contributes to organizing premier security conferences. He is a Senior Member of ACM and IEEE, and a Distinguished Speaker (ACM) and Distinguished Visitor (IEEE).