Keystroke dynamics-based user authentication using freely typed text based on user-adaptive feature extraction and novelty detection

• Published in: Applied soft computing 2018-01, Vol.62, p.1077-1087
• Main Authors: Kim, Junhong, Kim, Haedong, Kang, Pilsung
• Format: Article
• Language: English
• Subjects: Free text; Keystroke dynamics; Novelty detection; User authentication; User-adaptive features
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.09.045

•A user-adaptive feature extraction method is proposed.•Novelty detection algorithms are employed to build user authentication models.•The best equal error rate (EER) achieved by the proposed method is 0.44%.•The proposed method enhanced the user authentication performance by 45.3% for Korean and 39.0% for English compared with a benchmark fixed feature extraction method. Keystroke dynamics has been used to strengthen password-based user authentication systems by considering the typing characteristics of legitimate users. The main problem with login-based authentication systems is that they cannot authenticate users after login access is granted. To ensure continuous user authentication, keystroke dynamics collected from freely typed text during the login period has been utilized; however, the authentication performance was unsatisfactory. To enhance the performance of user authentication based on freely typed keystrokes, we propose a user-adaptive feature extraction method that captures individual users’ distinctive typing behaviors embedded in relative typing speeds for different digraphs. Based on experimental results obtained from 150 participants with more than 13,000 keystrokes per each user in two languages (Korean and English), the proposed method achieved the best equal error rate (0.44). Furthermore, the authentication performance was enhanced by 45.3% for Korean and 39.0% for English compared with the benchmark fixed feature extraction method.