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R²BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification

Authors: Tsimperidis, I., Yoo, P.D., Taha, K., Mylonas, A. and Katos, V.

Journal: IEEE Transactions on Cybernetics

Volume: 50

Issue: 2

Pages: 525-535

eISSN: 2168-2275

ISSN: 2168-2267

DOI: 10.1109/TCYB.2018.2869658

Abstract:

Over the past decade, keystroke-based pattern recognition techniques, as a forensic tool for behavioral biometrics, have gained increasing attention. Although a number of machine learning-based approaches have been proposed, they are limited in terms of their capability to recognize and profile a set of an individual's characteristics. In addition, up to today, their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other characteristics, such as the educational level. Educational level is an acquired user characteristic, which can improve targeted advertising, as well as provide valuable information in a digital forensic investigation, when it is known. In this context, this paper proposes a novel machine learning model, the randomized radial basis function network, which recognizes and profiles the educational level of an individual who stands behind the keyboard. The performance of the proposed model is evaluated by using the empirical data obtained by recording volunteers' keystrokes during their daily usage of a computer. Its performance is also compared with other well-referenced machine learning models using our keystroke dynamic datasets. Although the proposed model achieves high accuracy in educational level prediction of an unknown user, it suffers from high computational cost. For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the tradeoff between an accurate and a fast prediction. To the best of our knowledge, this is the first model in the literature that predicts the educational level of an individual based on the keystroke dynamics information only.

https://eprints.bournemouth.ac.uk/31382/

Source: Scopus

R2BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification.

Authors: Tsimperidis, I., Yoo, P.D., Taha, K., Mylonas, A. and Katos, V.

Journal: IEEE Trans Cybern

Volume: 50

Issue: 2

Pages: 525-535

eISSN: 2168-2275

DOI: 10.1109/TCYB.2018.2869658

Abstract:

Over the past decade, keystroke-based pattern recognition techniques, as a forensic tool for behavioral biometrics, have gained increasing attention. Although a number of machine learning-based approaches have been proposed, they are limited in terms of their capability to recognize and profile a set of an individual's characteristics. In addition, up to today, their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other characteristics, such as the educational level. Educational level is an acquired user characteristic, which can improve targeted advertising, as well as provide valuable information in a digital forensic investigation, when it is known. In this context, this paper proposes a novel machine learning model, the randomized radial basis function network, which recognizes and profiles the educational level of an individual who stands behind the keyboard. The performance of the proposed model is evaluated by using the empirical data obtained by recording volunteers' keystrokes during their daily usage of a computer. Its performance is also compared with other well-referenced machine learning models using our keystroke dynamic datasets. Although the proposed model achieves high accuracy in educational level prediction of an unknown user, it suffers from high computational cost. For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the tradeoff between an accurate and a fast prediction. To the best of our knowledge, this is the first model in the literature that predicts the educational level of an individual based on the keystroke dynamics information only.

https://eprints.bournemouth.ac.uk/31382/

Source: PubMed

R<SUP>2</SUP>BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification

Authors: Tsimperidis, I., Yoo, P.D., Taha, K., Mylonas, A. and Katos, V.

Journal: IEEE TRANSACTIONS ON CYBERNETICS

Volume: 50

Issue: 2

Pages: 525-535

eISSN: 2168-2275

ISSN: 2168-2267

DOI: 10.1109/TCYB.2018.2869658

https://eprints.bournemouth.ac.uk/31382/

Source: Web of Science (Lite)

R2BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification.

Authors: Tsimperidis, I., Yoo, P.D., Taha, K., Mylonas, A. and Katos, V.

Journal: IEEE Trans. Cybern.

Volume: 50

Pages: 525-535

DOI: 10.1109/TCYB.2018.2869658

https://eprints.bournemouth.ac.uk/31382/

Source: DBLP

R²BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification.

Authors: Tsimperidis, I., Yoo, P.D., Taha, K., Mylonas, A. and Katos, V.

Journal: IEEE transactions on cybernetics

Volume: 50

Issue: 2

Pages: 525-535

eISSN: 2168-2275

ISSN: 2168-2267

DOI: 10.1109/tcyb.2018.2869658

Abstract:

Over the past decade, keystroke-based pattern recognition techniques, as a forensic tool for behavioral biometrics, have gained increasing attention. Although a number of machine learning-based approaches have been proposed, they are limited in terms of their capability to recognize and profile a set of an individual's characteristics. In addition, up to today, their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other characteristics, such as the educational level. Educational level is an acquired user characteristic, which can improve targeted advertising, as well as provide valuable information in a digital forensic investigation, when it is known. In this context, this paper proposes a novel machine learning model, the randomized radial basis function network, which recognizes and profiles the educational level of an individual who stands behind the keyboard. The performance of the proposed model is evaluated by using the empirical data obtained by recording volunteers' keystrokes during their daily usage of a computer. Its performance is also compared with other well-referenced machine learning models using our keystroke dynamic datasets. Although the proposed model achieves high accuracy in educational level prediction of an unknown user, it suffers from high computational cost. For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the tradeoff between an accurate and a fast prediction. To the best of our knowledge, this is the first model in the literature that predicts the educational level of an individual based on the keystroke dynamics information only.

https://eprints.bournemouth.ac.uk/31382/

Source: Europe PubMed Central

R2BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification.

Authors: Tsimperidis, I., Yoo, P.D., Taha, K., Mylonas, A. and Katos, V.

Journal: IEEE Transactions on Cybernetics

Volume: 50

Issue: 2

Pages: 525-535

ISSN: 2168-2267

Abstract:

Over the past decade, keystroke-based pattern recognition techniques, as a forensic tool for behavioral biometrics, have gained increasing attention. Although a number of machine learning-based approaches have been proposed, they are limited in terms of their capability to recognize and profile a set of an individual's characteristics. In addition, up to today, their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other characteristics, such as the educational level. Educational level is an acquired user characteristic, which can improve targeted advertising, as well as provide valuable information in a digital forensic investigation, when it is known. In this context, this paper proposes a novel machine learning model, the randomized radial basis function network, which recognizes and profiles the educational level of an individual who stands behind the keyboard. The performance of the proposed model is evaluated by using the empirical data obtained by recording volunteers' keystrokes during their daily usage of a computer. Its performance is also compared with other well-referenced machine learning models using our keystroke dynamic datasets. Although the proposed model achieves high accuracy in educational level prediction of an unknown user, it suffers from high computational cost. For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the tradeoff between an accurate and a fast prediction. To the best of our knowledge, this is the first model in the literature that predicts the educational level of an individual based on the keystroke dynamics information only.

https://eprints.bournemouth.ac.uk/31382/

Source: BURO EPrints