Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach
Kiyani, A., Lasebae, A., Ali, K., Ur-Rehman, M. and Haq, B. 2020. Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach. IEEE Access. 8, pp. 156177-156189. https://doi.org/10.1109/ACCESS.2020.3019467
|Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach
|Kiyani, A., Lasebae, A., Ali, K., Ur-Rehman, M. and Haq, B.
User authentication is considered to be an important aspect of any cybersecurity program. However, one-time validation of user’s identity is not strong to provide resilient security throughout the user session. In this aspect, continuous monitoring of session is necessary to ensure that only legitimate user is accessing the system resources for entire session. In this paper, a true continuous user authentication system featuring keystroke dynamics behavioural biometric modality has been proposed and implemented. A novel method of authenticating the user on each action has been presented which decides the legitimacy of current user based on the confidence in the genuineness of each action. The 2-phase methodology, consisting of ensemble learning and robust recurrent confidence model(R-RCM), has been designed which employs a novel perception of two thresholds i.e., alert and final threshold. Proposed methodology classifies each action based on the probability score of ensemble classifier which is afterwards used along with hyperparameters of R-RCM to compute the current confidence in the genuineness of user. System decides if user can continue using the system or not based on new confidence value and final threshold. However, it tends to lock out imposter user more quickly if it reaches the alert threshold. Moreover, system has been validated with two different experimental settings and results are reported in terms of mean average number of genuine actions (ANGA) and average number of imposter actions(ANIA), whereby achieving the lowest mean ANIA with experimental setting II.
|Continuous user authentication, keystroke dynamics, ensemble learning, behavioural analysis, biometrics, security systems
|Institute of Electrical and Electronics Engineers (IEEE)
|26 Aug 2020
|08 Sep 2020
|Publication process dates
|04 Sep 2020
|27 Jul 2020
|17 Aug 2020
Continuous User Authentication Featuring Keystroke Dynamics Based on Robust Recurrent Confidence Model and Ensemble Learning Approach.pdf
This work is licensed under a Creative Commons Attribution 4.0 License.
Intelligent Biometric Systems for Secure Societies
|Digital Object Identifier (DOI)
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