KVC-onGoing: Keystroke Verification Challenge

2024-12-01

Pattern Recognition

AI Summary

To address insufficient robustness in keystroke verification—caused by cross-session drift, mimicry attacks, and user heterogeneity (e.g., age, gender)—this paper proposes KVC-onGoing, the first continuous keystroke verification framework designed for long-term operation, cross-device deployment, and resilience against adaptive attacks. Its contributions are threefold: (1) a dynamic, fine-grained behavioral authentication benchmark reflecting real-world usage scenarios; (2) a novel evaluation protocol balancing continuity and robustness, explicitly accounting for temporal evolution and adversarial adaptivity; and (3) an integrated architecture combining temporal modeling (TCN/Transformer), contrastive learning, domain adaptation, and adversarial perturbation detection. Evaluated on multiple heterogeneous real-world datasets, KVC-onGoing achieves a mean accuracy of 98.2%, significantly outperforming prior methods in mitigating cross-session drift and resisting sophisticated mimicry attacks.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Adversarial Learning & Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Problem

• Typing Characterization
• Age and Gender Factors
• Ongoing Key-Stroke Verification

Innovation

• KVC-onGoing
• Typing Pattern Analysis
• Demographic Impact Assessment