Intelligent zero trust architecture for 5G/6G networks: Principles, challenges, and the role of machine learning in the context of O-RAN K Ramezanpour, J Jagannath Computer Networks 217, 109358, 2022 | 135 | 2022 |
Security and privacy vulnerabilities of 5G/6G and WiFi 6: Survey and research directions from a coexistence perspective K Ramezanpour, J Jagannath, A Jagannath Computer Networks 221, 109515, 2023 | 64 | 2023 |
SCAUL: Power side-channel analysis with unsupervised learning K Ramezanpour, P Ampadu, W Diehl IEEE Transactions on Computers 69 (11), 1626-1638, 2020 | 56 | 2020 |
Digital twin virtualization with machine learning for IoT and beyond 5G networks: Research directions for security and optimal control J Jagannath, K Ramezanpour, A Jagannath Proceedings of the 2022 ACM workshop on wireless security and machine …, 2022 | 44 | 2022 |
A statistical fault analysis methodology for the ascon authenticated cipher K Ramezanpour, P Ampadu, W Diehl 2019 IEEE International Symposium on Hardware Oriented Security and Trust …, 2019 | 42 | 2019 |
FIMA: fault intensity map analysis K Ramezanpour, P Ampadu, W Diehl International Workshop on Constructive Side-Channel Analysis and Secure …, 2019 | 29 | 2019 |
SCARL: side-channel analysis with reinforcement learning on the ascon authenticated cipher K Ramezanpour, P Ampadu, W Diehl arXiv preprint arXiv:2006.03995, 2020 | 26 | 2020 |
Rs-mask: Random space masking as an integrated countermeasure against power and fault analysis K Ramezanpour, P Ampadu, W Diehl 2020 IEEE International Symposium on Hardware Oriented Security and Trust …, 2020 | 23 | 2020 |
Intelligent zero trust architecture for 5G/6G tactical networks: Principles, challenges, and the role of machine learning K Ramezanpour, J Jagannath arXiv preprint arXiv:2105.01478, 2021 | 19 | 2021 |
Active and passive side-channel key recovery attacks on Ascon K Ramezanpour, A Abdulgadir, W Diehl, JP Kaps, P Ampadu Proc. NIST Lightweight Cryptogr. Workshop, 1-27, 2020 | 14 | 2020 |
Fault intensity map analysis with neural network key distinguisher K Ramezanpour, P Ampadu, W Diehl Proceedings of the 3rd ACM Workshop on Attacks and Solutions in Hardware …, 2019 | 10 | 2019 |
Zero trust architecture for networks employing machine learning engines AL Drozd, J Jagannath, A Jagannath, K Ramezanpour US Patent 12,063,241, 2024 | 7 | 2024 |
Mr-inet gym: Framework for edge deployment of deep reinforcement learning on embedded software defined radio J Jagannath, K Hamedani, C Farquhar, K Ramezanpour, A Jagannath Proceedings of the 2022 ACM Workshop on Wireless Security and Machine …, 2022 | 7 | 2022 |
Intelligent zero trust architecture for 5g/6g networks: Principles K Ramezanpour, J Jagannath Challenges, and the Role of Machine Learning in the context of O-RAN. arXiv, 2021 | 7 | 2021 |
Analysis of Deep Learning Models Towards High Performance Digital Predistortion for RF Power Amplifiers R Kudupudi, FL Pour, DS Ha, SS Ha, K Ramezanpour 2022 IEEE International Symposium on Circuits and Systems (ISCAS), 1309-1313, 2022 | 1 | 2022 |
Reconfigurable Clock Generator with Wide Frequency Range and Single-Cycle Phase and Frequency Switching K Ramezanpour, P Ampadu 2018 31st IEEE International System-on-Chip Conference (SOCC), 206-212, 2018 | 1 | 2018 |
Reduction of multipath errors in spread-spectrum code tracking K Ramezanpour, BH Khalaj, A Fotowat-Ahmady 2010 IEEE International Conference on Communications, 1-6, 2010 | 1 | 2010 |
Authentication of device in network using cryptographic certificate K Ramezanpour, J Jagannath, A Jagannath, AL Drozd US Patent App. 18/165,667, 2023 | | 2023 |
System and methodology for secure coexistence between wireless fidelity and cellular networks J Jagannath, A Jagannath, AL Drozd, K Ramezanpour US Patent App. 17/822,458, 2023 | | 2023 |
A Deep Learning Approach to Side-Channel Analysis of Cryptographic Hardware K Ramezanpour Virginia Tech, 2020 | | 2020 |