Dynamic keystroke pattern analysis and classifiers with competence for user recognition

Piotr Porwik

doi:10.1016/j.asoc.2020.106902

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Dynamic keystroke pattern analysis and classifiers with competence for user recognition

2021-02-01

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Piotr Porwik *

Rafał Doroz

T Wesołowski

DOI：10.1016/j.asoc.2020.106902

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Abstract

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The classification of biometric data is always a difficult task because these data are often unstable and depends on behavioural condition of a person. Personal keystroke activity is difficult to imitate and can be used for identity authentication. In this article, we suggest that the analysis of keystroke dynamics can dramatically increase the level of security of a computer system without disturbing the user's comfort. First, the user's established behavioural profile is loaded and then this profile is continuously compared with the current use of the keyboard. If a user's temporary behavioural profile differs from their established profile, then the access to the computer is blocked. If a current profile of the user is recognized as legitimate, then profile can be dynamically changed, which allows to adjust profiles according to users abilities. Security based on keystroke dynamics analysis depends primarily on the quality of recognizing changes in a user's profile. Recognition is performed on the dynamic analysis of the feature vector recorded while using the keyboard. In the presented approach, we propose using classifiers with their competence selection, which significantly improves the recognition of users. To select the most appropriate classifiers, the competences of the classifiers in a pool are calculated and then, the most powerful classifiers are selected. Conducted experiments, supported by the proper statistical analysis, confirm the usefulness of the proposed strategy in authorized user and intruder recognition. (C) 2020 Elsevier B.V. All rights reserved.

Keywords:

Biometrics

Authentication

Classifier competence

Pool of classifiers

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