Bibliographic Details
| Title: |
Anomaly detection through keystroke and tap dynamics implemented via machine learning algorithms. |
| Authors: |
JAWED, Hani1, ZIAD, Zara1, KHAN, Muhammad Mubashir1 mmkhan@neduet.edu.pk, ASRAR, Maheen1 |
| Source: |
Turkish Journal of Electrical Engineering & Computer Sciences. 2018, Vol. 26 Issue 4, p1698-1709. 12p. |
| Subjects: |
Anomaly detection (Computer security), Keystroke timing authentication, Machine learning, Computer passwords, Windows (Graphical user interfaces), Security systems |
| 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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
