Publications
Peer-reviewed research across IEEE, Springer, and international venues.
Machine Unlearning-based Privacy-First Medical Imaging Framework for TB Detection
Proposes a privacy-first machine unlearning approach for medical imaging, allowing TB detection models to forget patient data on demand while preserving diagnostic performance.
Explainable AI and Quantum Security for Smart Homes Network Attack Classification
Combines explainable AI techniques with quantum cryptographic primitives for classifying and interpreting network attacks in smart home IoT environments.
Deep Learning and Explainable AI Framework for False Data Injection Attack Detection in Autonomous Vehicles
Develops a deep learning pipeline with integrated SHAP-based explainability for detecting false data injection attacks in autonomous vehicle sensor networks, providing both accuracy and interpretable model decisions.
* Presented in person at Tampa, Florida, USA
Q-ShielD: CV-QKD Framework for Secure Autonomous Vehicle Communications
Introduces a continuous-variable quantum key distribution framework for securing V2X communications in autonomous vehicle networks against eavesdropping and quantum-level adversarial attacks.
Quantum-Based Edge Intelligence Framework for IoT Healthcare Systems
Presents a quantum-assisted edge computing framework for real-time anomaly detection and secure data processing in IoT-enabled healthcare environments.
Quantum-Secured Explainable Machine Unlearning for Phishing Detection in IoT
Proposes a quantum-secured machine unlearning framework that enables IoT devices to selectively forget phishing-related training data while maintaining detection accuracy, using explainability techniques to audit the unlearning process.