Authors - Nithin Gattappagari, Lakshmi Sagar S, Reddy Lokesh K, Banu Prakash N, Asritha A, Varalakshmi U, Karthik P, Praveen Kumar Rayani Abstract - Conventional one-time authentication cannot prevent session hijacking after login. This paper proposes a session-level impostor de tection framework based on Siamese learning over mouse dynamics for continuous authentication. The model combines statistical behavioral de scriptors with lightweight temporal modeling (Conv1D+GRU) to learn compact embeddings for open-set verification. It supports one-shot en rollment by comparing a query session against a single verified reference session and stores non-reversible embeddings instead of raw trajectories to improve privacy. We evaluate on Balabit and SAPiMouse under se vere class imbalance using balanced batching, semi-hard negative mining, and focal contrastive loss. The framework achieves AUROC 0.95/0.96, F1 0.80/0.85, and accuracy 0.92/0.93, with 46K trainable parameters and approximately 15ms inference time, indicating practical deployment potential.
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