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Welcome! I am excited to join Towson University as an Assistant Professor in the Computer and Information Sciences department starting Fall 2023. I earned my Ph.D. in Software and Information Systems from the University of North Carolina at Charlotte, supervised by Dr. Jinpeng Wei, with co-advisement from Dr. Ehab Al-Shaer from Carnegie Mellon University. Previously, I worked for over 3.5 years as a Software Engineer, developing digital payment solutions, web applications, and automated test suites, delivering products used by thousands in Bangladesh, South Korea, and China.

My research focuses on System Security, Cyber Deception, and Data Analytics for Security, with a primary emphasis on Malware Analysis. I develop resilient systems to analyze and deceive malware through dynamic techniques like sandboxing, symbolic execution, and AI-powered reasoning. By applying machine learning, graph analysis, and text mining, I profile malware behaviors, map them to the MITRE ATT&CK framework, and extract deception parameters for systematic orchestration using methods like HoneyThings, API Hooking, and reinforcement learning. I also leverage Large Language Models (LLMs) to analyze malware behaviors from execution logs and event traces, enabling the design of deception systems to counter emerging threats. Additionally, my research extends to Threat Hunting and Threat Intelligence, applying AI and data analytics to proactively detect and mitigate cyber risks.

CV (PDF)»

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Publications Google Scholar»

• IEEE CCWC'25 "SPADE: Enhancing Adaptive Cyber Deception Strategies with Generative AI and Structured Prompt Engineering", Shihab Ahmed, ABM Rahman, Md Morshed Alam, and Md Sajidul Islam Sajid. 2025 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC), 2025.
• IEEE GLOBECOM'24 "Securing Proof of Stake Blockchains: Leveraging Multi-Agent Reinforcement Learning for Detecting and Mitigating Malicious Nodes", Faisal Haque Bappy, Kamrul Hasan, Md Sajidul Islam Sajid, Mir Mehedi Ahsan Pritom, and Tariqul Islam. 2024 IEEE Global Communications Conference (GLOBECOM), 2024.
• IEEE SecDev'24 "Moving from the Developer Machine to IoT Devices: An Empirical Study", Md Rabbi Alam, Jinpeng Wei, Md Sajidul Islam Sajid, Qingyang Wang, and Chenglong Fu. 2024 IEEE Secure Development Conference (SecDev), 2024.
• IEEE Blockchain'24 "Maximizing Blockchain Performance: Mitigating Conflicting Transactions through Parallelism and Dependency Management", Faisal Haque Bappy, Tarannum Shaila Zaman, Md Sajidul Islam Sajid, Mir Mehedi Ahsan Pritom, and Tariqul Islam. 2024 IEEE International Conference on Blockchain (Blockchain), 2024.
• IEEE CCWC'24 "MRL-PoS: A Multi-Agent Reinforcement Learning Based Proof of Stake Consensus Algorithm for Blockchain", Tariqul Islam, Faisal Haque Bappy, Tarannum Shaila Zaman, Md Sajidul Islam Sajid, and Mir Mehedi Ahsan Pritom. IEEE Computing and Communication Workshop and Conference (CCWC), January 2024. (Best paper award)
• IEEE CCNC'24 "ConChain: A Scheme for Contention-free and Attack Resilient BlockChain", Tariqul Islam, Faisal Haque Bappy, Tarannum Shaila Zaman, Mir Mehedi Ahsan Pritom, and Md Sajidul Islam Sajid. IEEE Consumer Communications & Networking Conference (CCNC), January 2024.
• ACM TOPS'23 "symbSODA: Configurable and Verifiable Orchestration Automation for Active Malware Deception", Md Sajidul Islam Sajid, Jinpeng Wei, Ehab Al-Shaer, Qi Duan, Basel Abdeen, and Latifur Khan. ACM Transactions on Privacy and Security, October 2023.
• IEEE WCNC'22 "IoTMonitor: A Hidden Markov Model-based Security System to Identify Crucial Attack Nodes in Trigger-action IoT Platforms", Md Morshed Alam, Md Sajidul Islam Sajid, Weichao Wang, and Jinpeng Wei. IEEE Wireless Communications and Networking Conference, January 2022.
• ACM ACSAC'21 "SODA: A System for Cyber Deception Orchestration and Automation", Md Sajidul Islam Sajid, Jinpeng Wei, Basel Abdeen, Ehab Al-Shaer, Md Mazharul Islam, Walter Diong, and Latifur Khan. ACM Annual Computer Security Applications Conference, August 2021.
• IEEE CNS'21 "CHIMERA: Autonomous Planning and Orchestration for Malware Deception", Md Mazharul Islam, Ashutosh Dutta, Md Sajidul Islam Sajid, Ehab Al-Shaer, Jinpeng Wei, and Sadegh Farhang. IEEE Conference on Communications and Network Security, October 2021.
• IEEE CNS'20 "DodgeTron: Towards Autonomous Cyber Deception Using Dynamic Hybrid Analysis of Malware", Md Sajidul Islam Sajid, Jinpeng Wei, Md Rabbi Alam, Ehsan Aghaei, and Ehab Al-Shaer. IEEE Conference on Communications and Network Security, June 2020.
• IJCNWC'14 "An Energy-Efficient Data Aggregation Tree Construction Algorithm for Wireless Sensor Networks", Md Sajidul Islam Sajid, Imtiaz Bin Rahim, and Mosarrat Jahan. International Journal of Computer Networks and Wireless Communications, October 2014.

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Grants More »

• PI, 2025 Towson University Foundation Grants: CyberAI Curriculum Development for Integrated AI and Cybersecurity Education
• Co-PI, 2024 NSF CyberCorps Scholarship for Service (Renewal) Grants: Workshops for Developing Knowledge Units in the CAE-AI Curriculum (award 1663184)
• PI, 2024 Towson University Faculty Development & Research Committee Grants: Advancing Ransomware Detection using Cyber Deception

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Courses More »

• COSC439 Operating Systems
• COSC440 Operating System Security
• COSC481 Case Studies in Computer Security

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Professional Service More »

• Reviewer, International World Wide Web Conference (2021 - 2023).
• Reviewer, IEEE Conference on Computer Communications (INFOCOM) (2022).
• Reviewer, International Conference on Information and Communications Security (ICICS) (2021).

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Prospective Students

I am seeking dedicated Ph.D. candidates who are passionate about Cybersecurity research and wish to embark on their Ph.D. journey at Towson University, starting in Spring/Fall 2025. If you have a keen interest in Cybersecurity, specifically focusing on Malware Analysis, Defense Strategies, and Deception-based System Orchestration, I encourage you to contact me at msajid@towson.edu. Please include your CV/Resume, GRE/TOEFL Scores, and a statement outlining your motivation to pursue a Ph.D. RA positions are available.

Preferred Qualities in Prospective Students:

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field (required) with experience or a background in cybersecurity.
• A Master's degree is strictly required.
• Proficiency in C++ (required) with significant knowledge and hands-on experience, which is beneficial for malware analysis, reverse engineering, and system orchestration.
• Research experience and prior publication(s) (required).
• Strong drive to conduct impactful research in the Cybersecurity field (required).
• Excellent problem-solving skills and a proactive approach.
• Previous experience working with large datasets, analyzing insights, and utilizing ML/DL models for detection, prediction, and forecasting (preferred).

My current prioritized research areas are as follows:

• Automated and Verifiable Deceptive System Orchestration through Systematic Planning: Cyber deception has always been a focal point of my research. One of the core challenges in orchestrating deceptive systems is understanding the system variables attackers rely on to achieve their goals. In the future, I will continue to work on effectively extracting deception parameters from malware or compromised systems using dynamic malware analysis and utilizing them to orchestrate deceptive environments. The next challenge lies in selecting optimal deception actions considering their effectiveness, costs, and overhead. Hence, I intend to combine active learning, such as reinforcement learning, with the deception planner to optimize deception planning. The outcome of this research will also enrich deception-based malware detection approaches.
• Advanced Automated Dynamic Malware Analysis and Reasoning: This project focuses on enhancing automated dynamic malware analysis to combat stealthy cyber-attacks. Our objective is to develop a sophisticated analysis framework using AI-powered symbolic execution, targeting the high-level behaviors of malware. The goal is to overcome existing limitations and expedite the process by guiding execution towards malicious objectives. Additionally, we will automate information extraction and reasoning using AI, CTIO, and NLP, ensuring an efficient and effective analysis of evolving threats.
• Leveraging LLM Models for Autonomous Understanding of Malware Behaviors and Designing Deception Systems: In this project, we aim to utilize Large Language Models (LLMs) like ChatGPT and employ prompt engineering techniques to comprehend malware behavior using diverse inputs such as malware execution logs, malicious subgraphs, and event logs. By leveraging LLMs' capabilities in understanding patterns and contextual information, we intend to extract actionable insights regarding malicious activities. Subsequently, we will utilize this knowledge to design and develop sophisticated deception-based systems that effectively counter attackers, enhancing cybersecurity measures in the evolving threat landscape.

Please note: If you are solely interested in ML/DL-based research, I kindly request that you refrain from contacting me. At present, I already have students dedicated to this area. Please reach out only if you are enthusiastic about system security and malware analysis.
While my ongoing projects focus on specific areas within cybersecurity, I am more than willing to explore and engage with proposals that may fall outside those projects but align with my expertise and knowledge. If you have a compelling research proposal that showcases creativity and innovation within the field of cybersecurity or related fields, I encourage you to reach out to me with a timeline and proposal. I am open to discussing potential collaborations and providing guidance to help bring your research vision to fruition.