AI-Enhanced Zero-Knowledge Authentication for High-Mobility IoT Using Predictive Token Learning
S. Ahmed, M. H. Anisi
IEEE Internet of Things Journal, vol. 13, no. 5, pp. 9068-9077
Cybersecurity researcher at the University of Essex, building provably secure, post-quantum authentication for smart grids, vehicles and IoT.

PhD dissertation submitted at the University of Essex, awaiting the degree.
Cybersecurity researcher with more than six years of academic, teaching, and research experience. My PhD research examines provably secure and cost-effective authentication for smart grids, vehicle-to-grid communication, and Internet of Things networks. The work combines applied cryptography, post-quantum security, AI-assisted authentication, federated learning, physical unclonable functions, and formal verification.
Authentication protocols, key agreement, signcryption, elliptic curve cryptography, and post-quantum cryptography
Smart grids, V2G communication, industrial cyber-physical systems, vehicular networks, and wireless sensor networks
Reinforcement learning, intrusion detection, zero-knowledge authentication, and predictive token learning
Federated learning, blockchain access control, PUFs, and edge computing security
S. Ahmed, M. H. Anisi
IEEE Internet of Things Journal, vol. 13, no. 5, pp. 9068-9077
S. Ahmed, M. H. Anisi
IEEE Transactions on Consumer Electronics, vol. 71, no. 3, pp. 8433-8440
S. Ahmed, M. H. Anisi
IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 8, pp. 12548-12557
Collaborations, speaking invitations, or a thoughtful disagreement with something I wrote.