Age Detection Through Keystroke Dynamics from User Authentication Failures

Age Detection Through Keystroke Dynamics from User Authentication Failures

Ioannis Tsimperidis (Democritus University of Thrace, Komotini, Greece), Shahin Rostami (Bournemouth University, Poole, UK) and Vasilios Katos (Bournemouth University, Poole, UK)
Copyright: © 2017 |Pages: 16
DOI: 10.4018/IJDCF.2017010101
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In this paper an incident response approach is proposed for handling detections of authentication failures in systems that employ dynamic biometric authentication and more specifically keystroke user recognition. The main component of the approach is a multi layer perceptron focusing on the age classification of a user. Empirical findings show that the classifier can detect the age of the subject with a probability that is far from the uniform random distribution, making the proposed method suitable for providing supporting yet circumstantial evidence during e-discovery.
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Artificial Neural networks were used in the past to classify computer users or computer items into some categories. For instance, Clark et al. (2003) presented an artificial neural network based system for automated e-mail filing into folders and anti-spam filtering. Nogueira et al (2005) and Auld et al (2007) dealt with Internet traffic. More specifically, Noguiera et al. (2005) proposed the classification of Internet users into groups according to their average transfer rate. Auld et al. (2007) classified flows based on header-derived statistics, and this is feasible even when the IP (host) address and application port number are not known.

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