Self-Supervised Session-Anomaly Detection for Password-less Wallet Logins
Keywords:
self-supervised learning, anomaly detection, contrastive learning, clickstream analytics, biometric telemetryAbstract
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.
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