Publications

• [J1] T. Karim*, M. S. Akter. "Vulnerability Datasets for Software Security: A Survey of Existing Resources, Challenges, and Future Directions." Computers & Security, Elsevier, 2026. (IF: 4.8, Q1)
• [J2] M. S. Hossain Shaon*, M. S. Akter. "Modern Approaches to Software Vulnerability Detection: A Survey of Machine Learning, Deep Learning, and Large Language Models." Electronics, MDPI, 2025. (IF: 2.9, Q1)
• [J3] M. F. Sultan*, T. Karim*, M. S. Hossain Shaon*, M. Wardat, M. S. Akter. "CodeGraphNet: Embedding-Driven Enhanced Vulnerability Detection with Line Level Error Highlighting." Software Quality Journal, Springer, 2025. (IF: 2.3, Q2)
• [J4] Q. Bataineh, Z. Hailat, Q. M. Al-Zubi, M. S. Akter, M. A. Zohdy. "Malicious JavaScript Detection Using Machine Learning: A Survey of Taxonomy, Trends, and Open Challenges." Cluster Computing, Springer. (Under Review) (IF: 5.5, Q1)

• [C1] S. S. Alam*, M. S. Akter, A. Cuzzocrea. "Improving Software Security Through a LLM-Based Vulnerability Detection Model." DEXA 2025, Springer, pp. 122–129. (Acceptance Rate: 36.5%)
• [C2] M. S. Akter, M. F. Sultan*, A. Cuzzocrea. "Neuro-Symbolic Methods in Natural Language Processing: A Review." DATA 2025, SCITEPRESS, pp. 274–282. (Acceptance Rate: 18.4%)
• [C3] T. Karim*, M. S. Hossain Shaon*, M. F. Sultan*, A. Cuzzocrea, M. S. Akter. "NULLDect: A Dynamic Adaptive Learning Framework for Robust NULL Pointer Dereference Detection." SECRYPT 2025, pp. 571–576. (Acceptance Rate: 16.75%)
• [C4] T. Karim*, M. S. Akter, A. Cuzzocrea. "A Benchmark Dataset for Code-Level Vulnerability Detection and Analysis." IEEE BigData 2025. (Acceptance Rate: 18.4%)
• [C5] M. S. Hossain Shaon*, M. S. Akter, A. Cuzzocrea. "ResVul-LLM: A Neurosymbolic Framework Combining Large Language Models and Symbolic Reasoning for C/C++ Vulnerability Analysis." IEEE BigData 2025. (Acceptance Rate: 18.4%)
• [C6] M. F. Sultan*, M. S. Akter, A. Cuzzocrea. "CodeVul+: A Structure-Aware Framework for Cross-Repository Vulnerability Detection." IEEE BigData 2025. (Acceptance Rate: 18.4%)
• [C7] M. F. Sultan*, M. S. Akter, A. Cuzzocrea. "P3R: Parallel Plugin-based Parameter Efficient Fine-tuning for Code Understanding through Hierarchical Representation Refinement." IEEE BigData 2025. (Acceptance Rate: 18.4%)
• [C8] S. Z. Ridoy, M. S. Hossain Shaon*, A. Cuzzocrea, M. S. Akter. "EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code." IEEE BigData 2024, pp. 6356–6364. (Acceptance Rate: 18.4%)
• [C9] M. S. Akter, M. A. Rahman, M. M. Rahman, J. Rodriguez-Cardenas, H. Shahriar, F. Wu, M. Rahman. "Authentic Learning Approach for Data Poisoning Vulnerability in LLMs." IEEE COMPSAC 2024, pp. 1504–1505. (Acceptance Rate: 23%)
• [C10] M. S. Akter, H. Shahriar, J. Rodriguez-Cardenas, F. Wu, V. Clincy. "Mitigating Insecure Outputs in Large Language Models (LLMs): A Practical Educational Module." IEEE COMPSAC 2024. (Acceptance Rate: 23%)
• [C11] E. Kaanan, T. Karim*, M. S. Hossain Shaon*, M. F. Sultan*, A. Cuzzocrea, M. S. Akter. "LLM-Based Approach for Buffer Overflow Detection in Source Code." ICCIT 2024, pp. 1898–1902. (Acceptance Rate: 31.1%)
• [C12] M. S. Akter, H. Shahriar, J. R. Cardenas, S. I. Ahamed, A. Cuzzocrea. "Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks." IEEE COMPSAC 2023, Turin, Italy. (Acceptance Rate: 23%)
• [C13] M. S. Akter, H. Shahriar. "Towards Unsupervised Learning based Denoising of Cyber Physical System Data to Mitigate Security Concerns." IEEE CNS 2022, Austin, TX. (Acceptance Rate: 28%)
• [C14] E. Kanaan, S. S. Alam*, M. S. Akter. "Survey of Machine Learning Techniques for Detecting Buffer Overflow Vulnerabilities." 8th International Conference on Engineering Research, 2025.
• [C15] C. Lamkin, M. S. Akter, H. Shahriar, G. Francia. "Architecture Design and Implementation of a Security Threat Data Sharing Platform." IEEE COMPSAC 2024, pp. 1–6. (Acceptance Rate: 23%)
• [C16] M. M. Rahman, M. D. A. Barek, A. K. I. Riad, M. A. Rahman, H. Shahriar, M. S. Akter. "Authentic Learning on DevOps Security with Labware: Git Hooks to Facilitate Automated Security Static Analysis." IEEE COMPSAC 2024. (Acceptance Rate: 23%)
• [C17] M. S. Akter, H. Shahriar, J. Rodriguez-Cardenas, et al. "Teaching DevOps Security Education with Hands-on Labware: Automated Detection of Security Weakness in Python." ISCAP 2023.
• [C18] M. S. Akter, H. Shahriar, J. Rodriguez-Cardenas, et al. "Authentic Learning Approach for Artificial Intelligence Systems Security and Privacy." IEEE COMPSAC 2023, Turin, Italy. (Acceptance Rate: 23%)
• [C19] K. Priyansh, R. Dimri, F. I. Anik, M. S. Akter, N. Sakib, H. Shahriar, Z. A. Bhuiyan. "DuRBIN: A Comprehensive Approach to Analysis and Detection of Emerging Threats Due to Network Intrusion." IEEE DASC 2022.
• [C20] M. S. Akter, M. A. Barek, M. M. Rahman, A. K. I. Riad, M. A. Rahman, M. R. Mia, H. Shahriar, W. Chu, S. I. Ahamed. "HIPAA Technical Compliance Evaluation of Laravel-based mHealth Apps." IEEE ICDH 2024, pp. 58–67. (Acceptance Rate: 25%)