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Lookup NU author(s): Dr Lei ShiORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This paper investigates whether natural mobile interaction patterns exhibit stable behavioural signatures for identity verification, addressing a critical gap in real-world behavioural data mining research. Using NUSA, a custom short-form video app, we collected multimodal interaction data from 10 participants across 665 unconstrained sessions over 16 days. We propose a 50-dimensional embedding encapsulating three core dimensions: motor coordination, contextual rhythm, engagement dynamics, and integrate a drift detection mechanism defined by 2σ deviation from user-specific baselines. Cross-validation yields a mean AUC of 0.91 ± 0.03 and demonstrates robust user separability, with inter-user distances 10× greater than intra-user distances. Notably, NUSA enables lightweight on-device authentication with an inference latency of ≤ 10 ms; this design ensures full transparency, eliminates disruptive user interaction interruptions, and mitigates key limitations inherent to black-box behavioural biometric systems. Our findings confirm that behavioural rhythm mined from natural interactions remains stable across contextual shifts, thereby establishing a critical bridge between behavioural data mining, mobile HCI, and system security domains.
Author(s): Jalilzade E, Shi L
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: The 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) special session on Data Science: Foundations and Applications
Year of Conference: 2026
Pages: 320–332
Online publication date: 25/06/2026
Acceptance date: 17/02/2026
Date deposited: 10/03/2026
Publisher: Springer
URL: https://doi.org/10.1007/978-981-92-1947-6_26
DOI: 10.1007/978-981-92-1947-6_26
ePrints DOI: 10.57711/s5n3-mm87
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science (LNCS) and Lecture Notes in Artificial Intelligence (LNAI) and Pacific-Asia Conference on Knowledge Discovery and Data Mining conference series
ISBN: 9789819219469
