Aydin Aysu

Bio

Dr. Aysu received his B.S degree in microelectronics engineering with a mathematics minor and his M.S degree in electrical engineering from Sabanci University, Istanbul, Turkey. He received his Ph.D. degree in computer engineering from Virginia Tech and was a post-doctoral researcher at the University of Texas at Austin before he joined NC State. Dr. Aysu conducts research on cybersecurity with an emphasis on hardware-based security. The focus of his research is the development of secure systems that prevent advanced cyber attacks targeting hardware vulnerabilities. To that end, his research interests cover applied cryptography, computer architecture, and digital hardware design. He also works on cybersecurity education and the societal impacts of cybersecurity.

Area(s) of Expertise

Computer Architecture and Systems

Publications

• A Hardware-Software Co-Design for the Discrete Gaussian Sampling of FALCON Digital Signature , 2024 IEEE INTERNATIONAL SYMPOSIUM ON HARDWARE ORIENTED SECURITY AND TRUST, HOST (2024)
• Enabling Secure and Efficient Sharing of Accelerators in Expeditionary Systems , Journal of Hardware and Systems Security (2024)
• Extended Abstract: Pre-Silicon Vulnerability Assessment for AI/ML Hardware , PROCEEDING OF THE GREAT LAKES SYMPOSIUM ON VLSI 2024, GLSVLSI 2024 (2024)
• Leaking secrets in homomorphic encryption with side-channel attacks , JOURNAL OF CRYPTOGRAPHIC ENGINEERING (2024)
• Masking FALCON’s Floating-Point Multiplication in Hardware , IACR Transactions on Cryptographic Hardware and Embedded Systems (2024)
• Pre-Silicon Side-Channel Analysis of AI/ML Systems , 2024 IEEE Physical Assurance and Inspection of Electronics (PAINE) (2024)
• RD-FAXID: Ransomware Detection with FPGA-Accelerated XGBoost , ACM TRANSACTIONS ON RECONFIGURABLE TECHNOLOGY AND SYSTEMS (2024)
• Side-Channel Aware Training for Commercial Machine Learning Accelerators , (2024)
• TPUXtract: An Exhaustive Hyperparameter Extraction Framework , IACR Transactions on Cryptographic Hardware and Embedded Systems (2024)
• A Full-Stack Approach for Side-Channel Secure ML Hardware , 2023 IEEE International Test Conference (ITC) (2023)

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