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Dr. Amir Alipour-Fanid is an expert in cybersecurity and machine learning, specializing in securing cyber-physical systems, 5G networks, IoT, and autonomous vehicles. His research integrates advanced machine learning techniques to enhance cybersecurity resilience, and his contributions extend to esteemed IEEE publications, grants, and technical program committees.
Experience
Education
Doctor of Philosophy in Electrical and Computer Engineering: Cybersecurity and Machine Learning, George Mason University, Fairfax, Virginia Thesis: Machine Learning for Wireless Cyber-Physical Systems Security Master of Science in Electrical Engineering: Communications, University of Tabriz, Tabriz, Iran Bachelor of Science in Electrical Engineering, Azad University, Ardabil, Iran
Roles
Chair of the Undergraduate Curriculum Committee, CSIT Department, University of the District of Columbia (UDC) Service to the Profession (Reviewer) Technical Program Committee (TPC) member: IEEE INFOCOM Demo EEE INFOCOM, 2017-19, 2021-24 IEEE CyberC, 2019, 2023 IEEE Transactions on Information Forensics and Security
Courses Taught
CSCI 504: Design & Anal Algorithms CYSE 440: Network Defense and Applied Network Monitoring CYSE 310: Cybersecurity Planning, Operations, and Incident Response Management CYSE 275: Principles of Cybersecurity and Security Management CSCI 352: Network Security CSCI 353: Information Security CYSE 100: Introduction to Cybersecurity & Information Assurance CSCI 434: Analysis Of Algorithms CSCI 345: Human-Computer Interaction
Expertise
Research Focus / Works in Progress
Dr. Amir Alipour-Fanid’s research focuses on the intersection of cybersecurity and machine learning, with an emphasis on securing cyber-physical systems, 5G communication networks, the Internet of Things (IoT), and connected and autonomous vehicles. He develops advanced machine learning models to detect and mitigate threats in these domains, ensuring the integrity and safety of these increasingly interconnected systems. His work combines theoretical frameworks for understanding security vulnerabilities with practical, applied machine learning techniques to create adaptive, real-time cybersecurity solutions. This approach aims to enhance the resilience of critical infrastructure against evolving cyber threats.
Impact
Selected Publications
Alipour-Fanid, A., Kacem, T., Dabaghchian, M., & Albanese, M, (2024). Utilizing online learning for both defense and DoS Attacks in CPS: A repeated game approach. In Proceedings of IEEE Global Communications Conference.
Alipour-Fanid, A., Dabaghchian, M., Jiao, L., & Zeng, K, (2024). Learning-based secure spectrum sharing for intelligent IoT networks. In Proceedings of the 2024 International Symposium on Quality Electronic Design.
Ghosh, A., Albanese, M., Mukherjee, P., & Alipour-Fanid, A. (2024). Improving the efficiency of intrusion detection systems by optimizing rule deployment across multiple IDSs. In Proceedings of SECRYPT.
Alipour-Fanid, A., Dabaghchian, M., Arora, R., & Zeng, K, (2022). Multiuser scheduling in centralized cognitive radio networks: A multi-armed bandit approach. IEEE Transactions on Cognitive Communications and Networking (TCCN), 8(1), 102-115.
Alipour-Fanid, A., Dabaghchian, M., & Zeng, K, (2022). Self-unaware adversarial multi-armed bandits with switching costs. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 32(8), 2907-2919.
Alipour-Fanid, A., Dabaghchian, M., Wang, N., Jiao, L., & Zeng, K, (2021). Online learning-based optimal defense against jamming attacks in multi-channel wireless CPS. IEEE Internet of Things Journal (IoT-J), 8(5), 3801-3814.
Selected Grants
CISE-MSI: RPEP. (2021-25). SaTC: HBCU artificial intelligence and cybersecurity (AI-CyS) research partnership.
NSF Convergence Accelerator Track G. (2022-23). Secure texting over non-cooperative networks and anti-jamming enhancement in 5G.
Recognitions
Outstanding Academic Achievement Award, George Mason University Provost Fellowship, George Mason University, 2017 Dean Fellowship, School of Engineering, George Mason University, 2015-16
