Mohammad Saidur Rahman
Unit: Computer Science, University of Texas at El Paso
Title: Assistant Professor
Research Expertise
Dr. Mohammad Saidur Rahman’s research spans machine learning, network security, endpoint security, and quantum security.
In endpoint security, his work focuses on intelligent malware classification and detection systems inspired by human learning. Leveraging continual learning (CL) techniques, his team develops adaptive systems that enhance malware detection while minimizing catastrophic forgetting and reducing computational and storage costs. Dr. Rahman is also working on a multi-modal cyber defense system integrating diverse security data, including logs and network traffic, to improve threat detection and response.
In network security, Dr. Rahman’s research uncovers side-channel vulnerabilities in encrypted network traffic, exploring privacy risks in secure messaging, privacy-preserving technologies, and streaming platforms. His team identifies subtle fingerprints in encrypted traffic and proposes defenses to protect critical systems.
In quantum security, Dr. Rahman investigates quantum-secure communication in classical and satellite networks. His research focuses on securing protocols against emerging threats and implementing post-quantum cryptography (PQC) and quantum key distribution (QKD) for resource-constrained devices, such as IoT systems and satellites, addressing challenges in securing systems with limited computational capacity.
Dr. Rahman’s work has been recognized in top-tier conferences and journals, including IEEE S&P, ACM CCS, PETS, IEEE TIFS, CoLLAs, AAAI, and QCNC.
Areas of Expertise/Keywords
Machine Learning, Endpoint Security, Network Security, Quantum Security
