Research Overview
My research lies at the intersection of artificial intelligence, cybersecurity, explainable machine learning, and intelligent network systems. I develop trustworthy AI solutions that enhance security, improve decision-making, and provide interpretable insights for complex cyber-physical environments.
Research Areas
AI-Driven Cybersecurity
Machine learning and deep learning approaches for intrusion detection, anomaly detection, and intelligent cyber defence.
Explainable Artificial Intelligence
Developing transparent and trustworthy AI systems using SHAP, LIME, Anchors, and Integrated Gradients.
Intelligent Network Systems
Research on scalable, adaptive, and secure network architectures for modern communication environments.
Healthcare AI
Explainable anomaly detection and predictive modelling for healthcare applications.
Current Research Projects
Explainable Anomaly Detection in Healthcare Systems
Development of interpretable machine learning models for cardiovascular disease prediction using SHAP, LIME, Anchors, and Integrated Gradients.
Intelligent Intrusion Detection in Intent-Driven Networks
Deep-learning-based intrusion detection systems leveraging CNN and LSTM architectures for real-time cyber threat detection.
Research Methodology
Data Collection
Large-scale cybersecurity and healthcare datasets.
AI Modelling
Deep learning, machine learning, and transformer models.
Explainability
SHAP, LIME, Anchors, and interpretable AI frameworks.