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

Kiyani, A. T., 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. (doi: 10.1109/ACCESS.2020.3019467)

[img]
Preview
Text
222940.pdf - Published Version
Available under License Creative Commons Attribution.

1MB

Abstract

User authentication is considered to be an important aspect of any cyber security 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 hyper-parameters of R-RCM to compute the current confidence in 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:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Ur Rehman, Dr Masood
Authors: Kiyani, A. T., Lasebae, A., Ali, K., Ur Rehman, M., and Haq, B.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Access
Publisher:IEEE
ISSN:2169-3536
ISSN (Online):2169-3536
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in IEEE Access 8:156177-156189
Publisher Policy:Reproduced under a Creative Commons licence

University Staff: Request a correction | Enlighten Editors: Update this record