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.
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References
Ahmed, A.A., Traore, I.: Biometric recognition based on free-text keystroke dynamics. IEEE Trans. Cybern. 44(4), 458–472 (2013)
Alsultan, A., Warwick, K., Wei, H.: Non-conventional keystroke dynamics for user authentication. Patt. Recog. Lett. 89, 53–59 (2017)
Ayotte, B., Banavar, M., Hou, D., Schuckers, S.: Fast free-text authentication via instance-based keystroke dynamics. IEEE Trans. Biomet. Behav. Identity Sci. 2(4), 377–387 (2020)
Çeker, H., Upadhyaya, S.: User authentication with keystroke dynamics in long-text data. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–6. IEEE (2016)
Kiyani, A.T., Lasebae, A., Ali, K.: Continuous user authentication based on deep neural networks. In: 2020 International Conference on UK-China Emerging Technologies (UCET), pp. 1–4. IEEE (2020)
Kiyani, A.T., Lasebae, A., Ali, K., Rehman, M.U., Haq, B.: Continuous user authentication featuring keystroke dynamics based on robust recurrent confidence model and ensemble learning approach. IEEE Access 8, 156177–156189 (2020)
Kiyani, A.T., Lasebae, A., Ali, K., Ur-Rehman, M.: Secure online banking with biometrics. In: 2019 International Conference on Advances in the Emerging Computing Technologies (AECT), pp. 1–6. IEEE (2020)
Lu, X., Zhang, S., Hui, P., Lio, P.: Continuous authentication by free-text keystroke based on CNN and RNN. Comput. Secur. 96, 101861 (2020)
Manandhar, R., Wolf, S., Borowczak, M.: One-class classification to continuously authenticate users based on keystroke timing dynamics. In: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), pp. 1259–1266. IEEE (2019)
Porwik, P., Doroz, R., Wesolowski, T.E.: Dynamic keystroke pattern analysis and classifiers with competence for user recognition. Appl. Soft Comput. 99, 106902 (2021)
Shepherd, S.: Continuous authentication by analysis of keyboard typing characteristics. In: European Convention on Security and Detection (1995)
Sun, Y., Ceker, H., Upadhyaya, S.: Shared keystroke dataset for continuous authentication. In: 2016 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6. IEEE (2016)
Tse, K.W., Hung, K.: User behavioral biometrics identification on mobile platform using multimodal fusion of keystroke and swipe dynamics and recurrent neural network. In: 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp. 262–267. IEEE (2020)
Wu, P.Y., Fang, C.C., Chang, J.M., Kung, S.Y.: Cost-effective kernel ridge regression implementation for keystroke-based active authentication system. IEEE Trans. Cybern. 47(11), 3916–3927 (2016)
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Kiyani, A.T., Lasebae, A., Ali, K., Alkhayyat, A., Haq, B., Naeem, B. (2022). Robust Continuous User Authentication System Using Long Short Term Memory Network for Healthcare. In: Ur Rehman, M., Zoha, A. (eds) Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-030-95593-9_22
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DOI: https://doi.org/10.1007/978-3-030-95593-9_22
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