Assistant Professor

Department: Computer Science

Dr. Mohammad Saidur Rahman is an Assistant Professor in the Department of Computer Science at the University of Texas at El Paso (UTEP). His research focuses on the intersection of machine learning, cybersecurity, and quantum security, with a primary focus on developing ML-driven intelligent systems for malware and network traffic analysis.

Among others, his work explores continual learning frameworks for improved malware detection and classification. His contributions also extend to large-scale malware datasets and binary analysis pipelines, where he combines machine learning with reverse engineering and program analysis to automate feature extraction from binaries and improve resilience against adversarial manipulation. In network security domain, Dr. Rahman examines how machine learning can both reveal and mitigate vulnerabilities in encrypted traffic, improving privacy and resilience. By designing attack and defense models, his work advances the understanding of information leakage and privacy risks in encrypted environments.

Furthermore, Dr. Rahman has led investigations into post-quantum cryptography (PQC) migration strategies for IoT devices, evaluating NIST candidate algorithms such as Kyber, BIKE, and HQC. His work also explores quantum key distribution (QKD) for satellite and anonymous communication networks like Tor, addressing real-world integration and security challenges.

Dr. Rahman's research contributions have been recognized in premier venues in security and privacy, including IEEE S&P, ACM CCS, PoPETS, and IEEE TIFS, as well as in leading machine learning conferences such as AAAI and CoLLAs, and in quantum communications venues, including IEEE QCNC and IEEE QCE. He has been recognized as a rising star in AI and ML for security by the IEEE TCCN SIG on Artificial Intelligence and Machine Learning in Security.

Other Affiliations: Computational Science Program, RIMES

Keywords of Expertise: Machine Learning, Endpoint Security, Network Security, Privacy, Quantum Security.