Detecting Identity Deception in Online Context

A Practical Approach Based on Keystroke Dynamics

Conference paper (2020)

Authors

Matteo Cardaioli GFT Italy, Università degli Studi di Padova
Merylin Monaro Università degli Studi di Padova
Giuseppe Sartori Università degli Studi di Padova
M. Conti Università degli Studi di Padova
Affiliation
External organisation
Identity verification Keystroke dynamics Lie detection
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Published Date

2020

Language

English

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Affiliation

External organisation

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.