Turkish Journal of Electrical Engineering and Computer Sciences
DOI
10.3906/elk-1711-410
Abstract
In our world of growing machine intelligence and increasing security risks, there is a dire need for authentication to be liberated from password dependency and restrictions. This paper discusses the implementation of keystroke biometrics to enhance security using machine-learning algorithms on both Windows and Android. Our research analyzes a user's behavior for authorization purposes by capturing the user's typing pattern. The system extracts several features from the user's typing pattern to apply unary classification for user behavior analysis so that we can detect unauthorized users. Our system implements machine learning on tap dynamics in Android, allowing both training and prediction and overcoming its computational restrictions.
Keywords
Keystroke dynamics, tap dynamics, user behavior analysis, one-class support vector machine, user authentication
First Page
1698
Last Page
1709
Recommended Citation
JAWED, HANI; ZIAD, ZARA; KHAN, MUHAMMAD MUBASHIR; and ASRAR, MAHEEN
(2018)
"Anomaly detection through keystroke and tap dynamics implemented via machine learning algorithms,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 26:
No.
4, Article 2.
https://doi.org/10.3906/elk-1711-410
Available at:
https://journals.tubitak.gov.tr/elektrik/vol26/iss4/2
Included in
Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons
