Sutharshan Rajasegarar

This paper proposes and evaluates a federated deep learning framework using autoencoders for lightweight, privacy-preserving, and scalable real-time anomaly detection in resource-constrained IoT networks.

The paper proposes Q-AGNN, a Quantum-Enhanced Attentive Graph Neural Network, to improve intrusion detection by modeling network flows as graphs and leveraging quantum circuits to capture complex relational dependencies.

This paper proposes an economic model to optimize the trade-off between entanglement distribution latency and decoherence time in distributed quantum computing.