Research Article
Robust Continuous User Authentication System Using Long Short Term Memory Network for Healthcare
@INPROCEEDINGS{10.1007/978-3-030-95593-9_22, author={Anum Tanveer Kiyani and Aboubaker Lasebae and Kamran Ali and Ahmed Alkhayyat and Bushra Haq and Bushra Naeem}, title={Robust Continuous User Authentication System Using Long Short Term Memory Network for Healthcare}, proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings}, proceedings_a={BODYNETS}, year={2022}, month={2}, keywords={Continuous authentication Periodic authentication Keystroke dynamics Recurrent neural network}, doi={10.1007/978-3-030-95593-9_22} }- Anum Tanveer Kiyani
Aboubaker Lasebae
Kamran Ali
Ahmed Alkhayyat
Bushra Haq
Bushra Naeem
Year: 2022
Robust Continuous User Authentication System Using Long Short Term Memory Network for Healthcare
BODYNETS
Springer
DOI: 10.1007/978-3-030-95593-9_22
Abstract
A traditional user authentication method comprises of username, passwords, tokens and PINs to validate the identity of user at initial login. However, a continuous monitoring method is needed for security of critical healthcare systems which can authenticate user on each action performed on system in order to ensure that only legitimate user i.e., genuine patient or medical employee is accessing the data from user account. In this aspect, the perception of employing behavioural patterns of user as biometric credential to incessantly re-verifying the user’s identity is being investigated in this research work to make the healthcare database information more secure. The keystroke behavioural biometric data represents the organisation of events in such a manner which resembles a time-series data, therefore, recurrent neural network is used to learn the hidden and unique features of users’ behaviour saved in time-series. Two different architectures based on per frame classification and integrated per frame-per sequence classification are employed to assess the system performance. The proposed novel integrated model combines the notion of authenticating user on each single action and on each sequence of actions. Therefore, firstly it gives no room to imposter user to perform any illicit activity as it authenticates user on each action and secondly it tends to include the advantage of hidden unique features related to specific user saved in a sequence of actions. Hence, it identifies the abnormal user behaviour more quickly in order to escalate the security especially in healthcare sector to secure the confidential medical data.
