Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach

Kiyani, Anum Tanveer, Lasebae, Aboubaker ORCID: https://orcid.org/0000-0003-2312-9694, Ali, Kamran ORCID: https://orcid.org/0000-0001-5301-9125, Ur-Rehman, Masood and Haq, Bushra (2020) Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach. IEEE Access, 8 . pp. 156177-156189. ISSN 2169-3536 [Article] (doi:10.1109/ACCESS.2020.3019467)

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Abstract

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.

Item Type: Article
Additional Information: Intelligent Biometric Systems for Secure Societies
Keywords (uncontrolled): Continuous user authentication, keystroke dynamics, ensemble learning, behavioural analysis, biometrics, security systems
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 30892
Notes on copyright: This work is licensed under a Creative Commons Attribution 4.0 License.
Useful Links:
Depositing User: Kamran Ali
Date Deposited: 04 Sep 2020 16:24
Last Modified: 07 Sep 2020 10:51
URI: https://eprints.mdx.ac.uk/id/eprint/30892

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