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Account-Sharing Detection Through Keystroke Dynamics Analysis
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DOI：10.2753/JEC1086-4415140204.png)
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Account sharing refers to a situation where multiple individuals share a Web site account to avoid paying a fee or providing personal information. As a result of account sharing, service providers lose revenue, underestimate membership, and have impaired understanding of their customers. A generic framework for detecting account sharing is proposed, using keystroke dynamics. Starting with the observation that a user's keystroke patterns are consistent and distinct from those of other individuals, it is assumed that each user's keystroke patterns form a cluster in Euclidean space. The number of sharers can be estimated by the number of clusters. In this paper, the optimal number of clusters is estimated based on the Bayesian model-selection framework with Gaussian mixture models obtained using the variational Bayesian approach. In a case study involving 25 passwords and 16 users, the proposed approach performed well in sharing detection, with a 2 percent false alarm rate, a 2 percent miss rate, and a total user estimation error of 7 percent. The proposed approach is viable and merits further investigation.
Keyword:
Account sharing
biometrics
clustering
keystroke dynamics
typing pattern
Web site account management
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