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I am an Assistant Professor of the Department of Computer Science at the University of Texas at El Paso (UTEP) from Fall 2024. At UTEP, I direct IQSeC-Lab. In Spring 2027, IQSeC-Lab will relocate to Old Dominion University, with its primary home in the Department of Electrical and Computer Engineering and affiliations with the School of Cybersecurity and the Department of Computer Science. I have obtained my PhD in Computing & Information Sciences from Rochester Institute of Technology (RIT), working with Professor Dr. Matthew Wright. During my tenure at graduate school (2016-2024), I worked at the ESL Global Cybersecurity Institute (former Center for Cybersecurity). I received my MS (August, 2018) in Computing Security from the Department of Cybersecurity at RIT. I received Bachelors degree in Management Information Systems (MIS) in 2016 from University of Dhaka, Bangladesh.

My work has been recognized on prestigious computer and communication security platforms, including IEEE S&P, ACM CCS, PoPETS, IEEE TIFS, and ACM TOPS, as well as at ML conferences like AAAI, ICLR, CAMLIS, and CoLLAs, and Quantum Communications conferences like IEEE QCNC and IEEE QCE. My recent inventions in the quantum network space have been submitted for US patents. I have been recognized as a rising star in AI and ML in Security by the IEEE TCCN Special Interest Group for AI and Machine Learning in Security. I have worked as a research intern at several industry research laboratories, including Mandiant (part of Google Cloud), Nokia Bell Labs, and Cisco Quantum Lab, leading to several collaborative research publications.

Besides research, I also enjoy travelling, fishing, swimming, gardening, and literature.

Rising Star in AI and ML in Security
I have been recognized as a rising star in AI and ML in Security by the IEEE TCCN Special Interest Group for AI and Machine Learning in Security.

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Research Summary

My research advances trustworthy machine learning for security and privacy, with a particular focus on endpoint and network security and on learning systems that can adapt to evolving threats through continual learning. Increasingly, my research is centered on quantum security, where I study the secure and practical adoption of post-quantum cryptography, the security and potential advantages of quantum machine learning, and quantum-secure communication. Across these areas, my goal is to develop cyber defense technologies that remain intelligent, trustworthy, and secure as both adversaries and computing paradigms evolve.

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Continual Learning Papers »

Preprints

• 2026 "FreeMOCA: Memory-Free Continual Learning for Malicious Code Analysis", Zahra Asadi, Haeseung Jeon, Sohyun Han, Md Mahmuduzzaman Kamol, Se Eun Oh, Mohammad Saidur Rahman, . [ArXiv Paper] [Code]
• 2026 "McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware", Md Mahmuduzzaman Kamol, Jesus Lopez, Saeefa Rubaiyet Nowmi, Emilia Rivas, Md Ahsanul Haque, Edward Raff, Aritran Piplai, Mohammad Saidur Rahman, . [ArXiv Paper]
• 2025 "CITADEL: A Semi-Supervised Active Learning Framework for Malware Detection Under Continuous Distribution Drift", Md Ahsanul Haque, Md Mahmuduzzaman Kamol, Ismail Hossain, Suresh Kumar Amalapuram, Vladik Kreinovich, Mohammad Saidur Rahman, . [ArXiv Paper]

Publications - Journals, Conferences, and Workshops

• 2026 "TabCL: Continual Malware Classification with Tabular-Aware Generation", Jimin Park, Haeseung Jeon, AHyun Ji, Aritran Piplai, Mohammad Saidur Rahman, Se Eun Oh, Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2026. [Paper]


• 2026 "Experimental Design and Performance Simulation of Core Components for BBM92 Entanglement-Based QKD", Viviana Cadena, Arifur R. Khan, Miralem Mehic, Jesus Lopez, Saeefa Rubaiyet Nowmi, Samee U. Khan, Mohammad Saidur Rahman, IEEE Internet Computing 2026.


• 2026 "SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness", Saeefa Rubaiyet Nowmi, Jesus Lopez, Md Mahmudul Alam Imon, Shahrooz Pouryousef, Mohammad Saidur Rahman, IEEE Symposium on Security & Privacy (S&P) 2026. [ArXiv Paper] [Code]


• 2026 "LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis", Md Ahsanul Haque, Ismail Hossain, Md Mahmuduzzaman Kamol, Md Jahangir Alam, Suresh Kumar Amalapuram, Sajedul Talukder, Mohammad Saidur Rahman, International Conference on Learning Representations (ICLR) 2026. [Paper] [Code]


• 2026 "PadNet: Defending Neural Networks Against Adversarial Examples", Armon Barton, Matthew Wright, Edgar Jatho, Shaikh Akib Shahriyar, Mohammad Saidur Rahman, Kantha Girish Gangadhara, Jiang Ming, ACM Transactions on Privacy and Security (TOPS) 2025. [Paper]


• 2025 "MADAR: Efficient Continual Learning for Malware Analysis with Distribution-Aware Replay", Mohammad Saidur Rahman , Scott Coull, Qi Yu, Matthew Wright, Conference on Applied Machine Learning in Information Security (CAMLIS) 2025. [ArXiv] [Code]


• 2025 "Evaluating Post-Quantum Cryptographic Algorithms on Resource-Constrained Devices", Jesus Lopez, Viviana Cadena, Mohammad Saidur Rahman, IEEE Quantum Week 2025 -- IEEE International Conference on Quantum Computing and Engineering (QCE) 2025. [Paper]


• 2025 "Towards Quantum Machine Learning for Malicious Code Analysis", Jesus Lopez, Saeefa Rubaiyet Nowmi, Viviana Cadena, Mohammad Saidur Rahman, IEEE Quantum Week 2025 -- IEEE International Workshop on Quantum Computing and Reinforcement Learning (QCRL), co-located with IEEE QCE 2025. [Paper]


• 2025 "Mixture of Ransomware Experts (MoRE): An Ensemble-Based Attribution Approach", Sebastian Caballero-Roa, Juan Diego Martinez-Paez, Mohammad Saidur Rahman, Mohamed Rahouti, Samee Khan, Nasir Ghani, IEEE World Forum on Public Safety Technology 2025.


• 2025 "MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification", Jimin Park, AHyun Ji, Minji Park, Mohammad Saidur Rahman, Se Eun Oh, AAAI Conference on Artificial Intelligence 2025. [Paper]


• 2024 "Quantum Secure Anonymous Communication Networks", Mohammad Saidur Rahman, Stephen DiAdamo, Miralem Mehic, and Charles Fleming, IEEE International Conference on Quantum Communications, Networking, and Computing (QCNC) 2024. [Paper]


• 2023 "SoK: A Critical Evaluation of Efficient Website Fingerprinting Defenses", Nate Mathews, James K Holland, Se Eun Oh, Mohammad Saidur Rahman, Nike Hopper, and Matthew Wright, IEEE Symposium on Security & Privacy (S&P) 2023. [Paper] [Video] [Code]


• 2022 "On the Limitations of Continual Learning for Malware Classification", Mohammad Saidur Rahman, Scott Coull, and Matthew Wright, First Conference on Lifelong Learning Agents - CoLLAs 2022. [Paper] [Video] [Code]


• 2022 "Transformers for End-to-End InfoSec Tasks: A Feasibility Study", Ethan M. Rudd, Mohammad Saidur Rahman, and Philip Tully, ACM Workshop on Robust Malware Analysis (WoRMA) 2022 (co-located with ACM AsiaCCS 2022). [Paper]


• 2022 "DeepCoFFEA: Improved Flow Correlation Attacks on Tor via Metric Learning and Amplification", Se Eun Oh, Taiji Yang, Nate Mathews, James K Holland, Mohammad Saidur Rahman, Nike Hopper, and Matthew Wright, IEEE Symposium on Security & Privacy (S&P) 2022. [Paper] [Video] [Code]


• 2021 "GANDaLF: GAN for Data-Limited Fingerprinting", Se Eun Oh, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright, and Nike Hopper, Privacy Enhancing Technologies Symposium (PETS), 2021. [Paper] [Video] [Code]


• 2021 "Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces.", Mohammad Saidur Rahman, Mohsen Imani, Nate Mathews, and Matthew Wright, IEEE Transactions on Information Forensics and Security (TIFS), Volume 16, 2021. [Paper] [Code]
  Media:


• 2020 "Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks", Mohammad Saidur Rahman, Payap Sirinam, Nate Mathews, Kantha Girish Gangadhara, and Matthew Wright, Privacy Enhancing Technologies Symposium (PETS), 2020. [Paper] [Video] [Code]


• 2019 "Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-shot Learning", Payap Sirinam, Nate Mathews, Mohammad Saidur Rahman, and Matthew Wright, ACM SIGSAC Conference on Computer and Communications Security (CCS), 2019. [Paper] [Code]

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Referred Posters

• 2025 "Towards Robust Quantum Machine Learning", Saeefa Rubaiyet Nowmi, Jesus Lopez, Md Mahmudul Alam Imon, Viviana Cadena, Shahrooz Pouryousef, Mohammad Saidur Rahman, IEEE Quantum Week 2025 -- IEEE QCE 2025. [Paper]


• 2025 "Evaluating Post-Quantum Cryptographic Algorithms on Resource-Constrained Devices (poster version)", Jesus Lopez, Viviana Cadena, Mohammad Saidur Rahman, IEEE Quantum Week 2025 -- IEEE QCE 2025. [Paper]


• ACSAC 2024 "MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification", Jimin Park, Minji Park, AHyun Ji, Se Eun Oh, Mohammad Saidur Rahman, The Annual Computer Security Applications Conference (ACSAC) 2024.
• S&P 2023 "Towards Continual Learning for Malware Classification", Mohammad Saidur Rahman, Scott E. Coull, Matthew Wright, IEEE Symposium on Security & Privacy (S&P) 2023.
• CCS 2019 "Video Fingerprinting in Tor", Mohammad Saidur Rahman, Nate Mathews, and Matthew Wright, ACM SIGSAC Conference on Computer and Communications Security (CCS) 2019.
• CCS 2019 "Evaluating Security Metrics for Website Fingerprinting", Nate Mathews, Mohammad Saidur Rahman,and Matthew Wright, ACM SIGSAC Conference on Computer and Communications Security (CCS) 2019.
• CCS 2018 "Adversarial Traces for Website Fingerprinting Defense", Mohsen Imani, Mohammad Saidur Rahman,and Matthew Wright, ACM SIGSAC Conference on Computer and Communications Security (CCS) 2018.
• S&P 2018 "Using Packet Timing in Website Fingerprinting", Mohammad Saidur Rahman, Kantha Girish Gangadhara, Payap Sirinam, Matthew Wright, IEEE Symposium on Security & Privacy (S&P) 2018.
• S&P 2018 "Website Fingerprinting Attacks with Timing-based Features using Capsule Networks", Payap Sirinam, Mohammad Saidur Rahman, Kantha Girish Gangadhara, Matthew Wright, IEEE Symposium on Security & Privacy (S&P) 2018.

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Awards & Grants

• CCS 2019 Travel grant received to attend ACM Conference on Computer and Communications Security (CCS) 2019.
• UpStat 2019 Bronze Medal, Category: Computations & Applications, 8th Annual Conference of the UPSTATE Chapters of the American Statistical Association.
• RIT Winner , 3-Minutes Thesis Presentation Competition 2018.
• RIT Winner , Graduate Research Showcase 2017.

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Professional Services

Program Committee (PC)

• 2027 IEEE Symposium on Security and Privacy (S&P).
• 2027 Privacy Enhancing Technologies Symposium (PETS).
• 2027ACM ASIA Conference on Computer and Communications Security (AsiaCCS).
• 2026Annual Computer Security Applications Conference (ACSAC).
• 2026 IEEE European Symposium on Security and Privacy (EuroS&P).
• 2025 AAAI 2025.
• 2026 Privacy Enhancing Technologies Symposium (PETS) 2026.
• 2025 The Conference on Applied Machine Learning in Information Security (CAMLIS).
• 2025 ACM QSec: Quantum Security and Privacy Workshop 2025, Co-located with ACM CCS 2025.
• 2025 Workshop on Rethinking Malware Analysis (WoRMA).
• 2025 IEEE European Symposium on Security and Privacy (EuroS&P).
• 2024 The Conference on Applied Machine Learning in Information Security (CAMLIS) .
• 2022 The 21st Annual Scientific Computing with Python Conference (SciPy).
• 2021 International Conference on Emerging Security Information, Systems and Technologies (SECURWARE).

Reviewer

• 2026 NeurIPS.
• 2026 Nature.
• 2025 International Conference on Learning Representations (ICLR).
• 2023 Privacy Enhancing Technologies Symposium (PETS).
• 2022 Privacy Enhancing Technologies Symposium (PETS).
• 2021 Privacy Enhancing Technologies Symposium (PETS).
• 2021 USENIX Security Artifact Evaluation Committee.
• IEEE Transactions on Information Forensics and Security (TIFS).
• IEEE Transactions on Neural Networks and Learning Systems (TNNLS).
• IEEE Transactions on Dependable and Secure Computing (TDSC).
• IEEE Transactions on Network and Service Management (TNSM).
• Nature Scientific Reports.
• Security and Communication Networks Journal.
• Elsevier Computers & Security Journal.
• IEEE Access.