Mohammad Ashiqur Rahman
Assistant Professor, ECE
Dr. Mohammad Ashiqur Rahman is an Assistant Professor in the Department of Electrical and Computer Engineering at Florida International University. Before joining FIU, he was an Assistant Professor at Tennessee Tech University. He obtained a PhD in computing and information systems from the University of North Carolina at Charlotte (UNC Charlotte) in 2015. Previously, he received BS and MS in computer science and engineering from Bangladesh University of Engineering and Technology (BUET), Dhaka, in 2004 and 2007, respectively. Dr. Rahman’s primary research interests cover a wide area of computer networks and cyber-physical systems (CPS). His research focus primarily includes computer and information security, risk analysis and security hardening, secure and dependable resource allocation, and distributed computing. His research is primarily funded by NSF, DOE, and DOD. He is currently leading multiple grants on CPS security. Dr. Rahman coauthored a book and several book chapters and published over 100 peer-reviewed journal and conference papers. He served on the organization and technical program committees (TPCs) for various IEEE and ACM conferences. He currently serves as the TPC Co-Chair of IEEE/IFIP NOMS 2023.
As cyber-physical systems (CPSs) evolved with the increasing availability of the internet of things (IoT) and improved communication infrastructures, their security has been a major concern to both practitioners and academicians. Empirical analysis or systematic verification of anticipated attacks, often considering control functions and attack properties in isolation, are not suitable for adequately realizing the threat space or making cost-effective hardening. Formal reasoning-based analytics have been proven advantageous for threat analysis because they are provable and noninvasive and have the power to model the system holistically. ML-based control techniques are increasingly used in modern IoT/CPSs, which often introduce different threat characteristics than typical physics-based controllers. In particular, such ML-based control functions lack systematic mathematical analyses. Therefore, we require mechanisms to conceptualize the control logic from the ML models to facilitate formal threat synthesis concerning such controllers. There are more challenges. Although formal reasoning allows comprehensive, noninvasive modeling and logically proven analysis, solving such a synthesis-based model to list many resiliency threats often become computationally expensive. To augment the time efficiency of the resiliency analysis, we need intelligent mechanisms that can learn from formally/empirically synthesized attack vectors and apply effective measures to explore threats efficiently. This talk will present a few recent works that develop efficient tools addressing these challenges.
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