Cybersecurity
Protecting systems, networks and data from attacks, intrusion and misuse.
Md. Wahidur Rahman
Ph.D. Student in Electrical Engineering and Computer Science
Texas A&M University–Kingsville Student Member, IEEE
Research at the intersection of artificial intelligence and cybersecurity — intrusion detection, cross-platform malware analysis, privacy-preserving learning and intelligent healthcare.
FocusFocus areas: Cybersecurity, Intrusion Detection, Cross-Platform Malware Analysis, Federated Learning, Privacy-Preserving AI, Explainable AI, Medical AI, Computer Vision
Currently researchingFedQDFU: Non-IID Federated Quantum Learning for Diabetic Foot Ulcer Classification from Deep Image Features
Research Impact
All publications →- Total Publications
- 54
- Journal Articles
- 38
- Conference Papers
- 13
- Book Chapters
- 3
- Research Areas
- 11
- Students Supervised
- 1
- Citations
- 1,546
- h-index
- 19
- i10-index
- 29
- Journal articles3870%
- Conference papers1324%
- Book chapters36%
Publication counts and charts are generated from the bibliography on this site. Citation metrics entered manually from Google Scholar · Last updated September 23, 2026.
Research Themes
Research overview →Intrusion Detection
Identifying malicious activity in network traffic and system behaviour.
Cross-Platform Malware Analysis
Detecting and characterising malicious software across Windows and Android.
Federated Learning
Training models collaboratively without centralising raw data.
Privacy-Preserving AI
Machine learning that limits the exposure of sensitive or personal data.
Explainable AI
Making model decisions transparent and interpretable to people.
Recent Publications
View all publications →-
Bacterial Foraging Optimization-boosted convolutional neural network for brain tumor detection using MRI images
Journal of Computational Science, vol. 99, Art. no. 102918
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GXMalDetect: A Hybrid GA--XGBoost Architecture for Malware Detection Using Static Image Features
2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), pp. 1–6
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TabNet-IDS: A TabNet-Driven Tabular Deep Learning Framework for Intrusion Detection Systems
2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), pp. 1–6
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Privacy-Preserving Federated Deep Learning for Nomophobia Risk Prediction From Smartphone Usage Logs
2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), pp. 1–6