Age Detection Through Keystroke Dynamics from User Authentication Failures

Authors: Tsimperidis, G., Katos, V. and Rostami, S.

http://eprints.bournemouth.ac.uk/25123/

http://www.igi-global.com/journal/international-journal-digital-crime-forensics/1112

Journal: International Journal of Digital Crime and Forensics (IJDCF)

Volume: 9

Issue: 1

Publisher: IGI Global

ISSN: 1941-6210

DOI: 10.4018/IJDCF.2017010101

In this paper an incident response approach is proposed for handling detections of authentication failures in systems that employ dynamic biometric authentication and more specifically keystroke user recognition. The main component of the approach is a multi layer perceptron focusing on the age classification of a user. Empirical findings show that the classifier can detect the age of the subject with a probability that is far from the uniform random distribution, making the proposed method suitable for providing supporting yet circumstantial evidence during e-discovery.

This data was imported from Scopus:

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

http://eprints.bournemouth.ac.uk/25123/

Journal: International Journal of Digital Crime and Forensics

Volume: 9

Issue: 1

Pages: 1-16

eISSN: 1941-6229

ISSN: 1941-6210

DOI: 10.4018/IJDCF.2017010101

This source preferred by Vasilis Katos and Shahin Rostami

This data was imported from Web of Science (Lite):

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

http://eprints.bournemouth.ac.uk/25123/

Journal: INTERNATIONAL JOURNAL OF DIGITAL CRIME AND FORENSICS

Volume: 9

Issue: 1

Pages: 1-16

eISSN: 1941-6229

ISSN: 1941-6210

DOI: 10.4018/IJDCF.2017010101

The data on this page was last updated at 04:46 on July 24, 2017.