Abstract
Keystroke dynamics has been recently proved to be an effective behavioral measure to detect subjects who provide false demographic information in online contexts. However, current techniques still suffer from some limits that restrict their practical application, such as the use of errors as a key feature to train the lie detectors and the absence of normalized features. Here, an extension of a keystroke dynamics technique, which was recently proposed to detect faked identities, is reported with the goal to overcome these limitations. Using a Quadratic Discriminant Analysis an accuracy up to 92% in the identification of faked identities has been reached, even if errors were excluded from predictors and normalized features were included. The classification model performs similarly to those previously proposed, with a slightly lower accuracy (−3%) but overcoming their important practical limitations.
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Cardaioli, M., Monaro, M., Sartori, G., Conti, M. (2020). Detecting Identity Deception in Online Context: A Practical Approach Based on Keystroke Dynamics. In: Corradini, I., Nardelli, E., Ahram, T. (eds) Advances in Human Factors in Cybersecurity. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1219. Springer, Cham. https://doi.org/10.1007/978-3-030-52581-1_6
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DOI: https://doi.org/10.1007/978-3-030-52581-1_6
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