Robust continuous user authentication system using long short term memory network for healthcare
Tanveer, A., Lasebae, A., Ali, K., Alkhayyat, A., Ur-Rehman, M., Haq, B. and Naeem, B. 2021. Robust continuous user authentication system using long short term memory network for healthcare. Ur-Rehman, M. and Zoha, A. (ed.) 16th EAI International Conference on Body Area Networks. Glasgow, UK (Online) 25 - 26 Oct 2021 Cham Springer. https://doi.org/10.1007/978-3-030-95593-9_22
|Robust continuous user authentication system using long short term memory network for healthcare
|Tanveer, A., Lasebae, A., Ali, K., Alkhayyat, A., Ur-Rehman, M., Haq, B. and Naeem, B.
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 the security of critical healthcare systems which can authenticate user on each action performed on the system in order to ensure that only legitimate user i.e., a 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, the recurrent neural network is used to learn the hidden and unique features of users’ behaviour saved in timeseries. 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 users 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.
|16th EAI International Conference on Body Area Networks
|Body Area Networks. Smart IoT and Big Data for Intelligent Health: 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings
|Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
|Ur-Rehman, M. and Zoha, A.
|Place of publication
|10 Feb 2022
|11 Feb 2022
|Publication process dates
|25 Jan 2022
|09 Aug 2021
|25 Oct 2021
|Accepted author manuscript
File Access Level
|Digital Object Identifier (DOI)
|Web address (URL) of conference proceedings
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