User Attribution Through Keystroke Dynamics-Based Author Age Estimation

Authors: Tsimperidis, I., Rostami, S., Wilson, K. and Katos, V.

Journal: Lecture Notes in Networks and Systems

Volume: 180

Pages: 47-61

eISSN: 2367-3389

ISBN: 9783030647575

ISSN: 2367-3370

DOI: 10.1007/978-3-030-64758-2_4

Abstract:

Keystroke dynamics analysis has often been used in user authentication. In this work, it is used to classify users according to their age. The authors have extended their previous research in which they managed to identify the age group that a user belongs to with an accuracy of 66.1%. The main changes made were the use of a larger dataset, which resulted from a new volunteer recording phase, the exploitation of more keystroke dynamics features, and the use of a procedure for selecting those features that can best distinguish users according to their age. Five machine learning models were used for the classification, and their performance in relation to the number of features involved was tested. As a result of these changes in the research method, an improvement in the performance of the proposed system has been achieved. The accuracy of the improved system is 89.7%.

http://eprints.bournemouth.ac.uk/36304/

Source: Scopus

User Attribution Through Keystroke Dynamics-Based Author Age Estimation

Authors: Tsimperidis, I., Rostami, S., Wilson, K. and Katos, V.

Conference: INC 2020: 12th International Networking Conference

Pages: 47-61

ISBN: 9783030647575

ISSN: 2367-3370

Abstract:

Keystroke dynamics analysis has often been used in user authentication. In this work, it is used to classify users according to their age. The authors have extended their previous research in which they managed to identify the age group that a user belongs to with an accuracy of 66.1%. The main changes made were the use of a larger dataset, which resulted from a new volunteer recording phase, the exploitation of more keystroke dynamics features, and the use of a procedure for selecting those features that can best distinguish users according to their age. Five machine learning models were used for the classification, and their performance in relation to the number of features involved was tested. As a result of these changes in the research method, an improvement in the performance of the proposed system has been achieved. The accuracy of the improved system is 89.7%.

http://eprints.bournemouth.ac.uk/36304/

Source: BURO EPrints