A machine learning approach to keystroke dynamics based user authentication. (10th May 2007)

• Record Type: Journal Article
• Authors: Revett, Kenneth; Gorunescu, Florin; Gorunescu, Marina; Ene, Marius; Magalhaes, Sergio; Santos, Henrique
• Is Part Of: International journal of electronic security and digital forensics. Volume 1:Number 1(2007)
• Page Start: 55
• Page End: 70
• Publication Date: 2007-05-10
• Subjects: biometrics -- equal error rate -- EER -- keystroke dynamics -- probabilistic neural networks -- PNNs -- machine learning -- user authentication -- access control -- electronic security -- e-security -- internet -- attribute selection; Computer crimes -- Periodicals; Computer crimes -- Investigation -- Periodicals; Computer security -- Periodicals; Electronic commerce -- Law and legislation -- Periodicals; Information warfare -- Periodicals; 005.805
• Languages: English
• ISSNs: 1751-911X

Abstract

The majority of computer systems employ a login ID and password as the principal method for access security. In stand-alone situations, this level of security may be adequate, but when computers are connected to the internet, the vulnerability to a security breach is increased. In order to reduce vulnerability to attack, biometric solutions have been employed. In this paper, we investigate the use of a behavioural biometric based on keystroke dynamics. Although there are several implementations of keystroke dynamics available, their effectiveness is variable and dependent on the data sample and its acquisition methodology. The results from this study indicate that the Equal Error Rate (EER) is significantly influenced by the attribute selection process and to a lesser extent on the authentication algorithm employed. Our results also provide evidence that a Probabilistic Neural Network (PNN) can be superior in terms of reduced training time and classification accuracy when compared with a typical MLFN back-propagation trained neural network.