Dr. Alam completed his Ph.D. in Computing and Information Systems at the University of North Carolina at Charlotte in Summer 2024. His doctoral dissertation focused on modeling cyberattacks in Internet of Things (IoT) platforms and developing AI-based real-time defense solutions. He is passionate about using AI in Cyber Defense and currently serves as a 2024-2025 AI Teaching Fellow at 91ÖÆƬ³§.
Ph.D. in Computing and Information Systems, University of North Carolina at Charlotte, (2024)
Expertise
Research Interests
IoT Security, Cyber Physical Systems, Adversarial Machine Learning, Deep Reinforcement Learning
Articles
- Alam, M. and Wang, W. (2021). A comprehensive survey on data provenance: State-of-the-art approaches and their deployments for IoT security enforcement. J. Comput. Secur. 29 (4) , pp. 423–446.
Conference Proceeding
- Alam, M., Mohaimenur Rahman, A B M and Wang, W. (2024). IoTHaven: An Online Defense System to Mitigate Remote Injection Attacks in Trigger-action IoT Platforms 2024 IEEE 30th International Symposium on Local and Metropolitan Area Networks (LANMAN) (pp. 15-20).
- Alam, M., Jahan, I. and Wang, W. (2024). IoTWarden: A Deep Reinforcement Learning Based Real-Time Defense System to Mitigate Trigger-Action IoT Attacks 2024 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6).
- Alam, M., Sajid, M. Sajidul Islam., Wang, W. and Wei, J. (2022). IoTMonitor: A Hidden Markov Model-based Security System to Identify Crucial Attack Nodes in Trigger-action IoT Platforms 2022 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1695-1700).
- 2024: AI Teaching Fellowship, 91ÖÆƬ³§