Self-Supervised Session-Anomaly Detection for Password-less Wallet Logins - Vijay Kumar Soni; Vijaya Bhaskara Rao Kotapati; Jawaharbabu Jeyaraman (Newark Journal of Human-Centric AI and Robotics Interaction, 2025)
The objective of this paper is to present a self-supervised anomaly detection framework contrastive learning that predicts baseline clickstream patterns, biometric sequences, and device-posture telemetry for password-less wallet login without attack data. Latent embedding space holds regular session representations in which Kullback-Leibler divergence from taught priors indicates aggression. This system silently performs biometric liveness or behavioural re-authentication before approving the transaction when the deviation threshold is reached.