Secure Healthcare Monitoring and Attack Detection Framework using ELUS-BILSTM and STECAES

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Published Dec 17, 2024
https://doi.org/10.47164/ijngc.v15i3.1545
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Volume 15, Issue 3, November 2024

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Y. Jani
PG and Research Department of Computer Science, Muslim Arts College, Thiruvithancode. Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli – 627012, Tamil Nadu, India
P. Raajan
PG and Research Department of Computer Science, Muslim Arts College, Thiruvithancode. Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli – 627012, Tamil Nadu, India

Abstract

The patterns of providing health-centric services have transformed extremely with the enhancement along with innovations in mobile and wireless communication technologies subsuming the Internet of Things (IoT). Due to the rapidly increasing attack, the doctors were not provided with an accurate alerting mechanism by the prevailing health monitoring system. Thus, by utilizing the Exponential Linear activation Units-centred Bidirectional Long Short Term Memory (ELUS-BiLSTM) technique, a novel healthcare monitoring along with an attack detection system is proposed in this work. Attack detection, Data security, and Patient health monitoring are the three primary phases incorporated in the proposed methodology. Initially, from the patient, the data are collected, and then the features are extracted in the attack detection phase. Next, the features being extracted are inputted to the ELUS-BiLSTM classifier where the data is classified as attacked or non-attacked data. After that, by utilizing Skew Tent Elliptic Curve Advanced Encryption Standard (STECAES), the non-attacked data is encrypted whereas the attacked data is stored in the log file. Lastly, to generate the fuzzy rules, the encrypted data is utilized; subsequently, the alert message is sent to the doctor. The experiential outcomes displayed that when analogized with the prevailing methodologies, the proposed model obtained better outcomes.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite
Jani, Y., & P. Raajan. (2024). Secure Healthcare Monitoring and Attack Detection Framework using ELUS-BILSTM and STECAES. International Journal of Next-Generation Computing, 15(3). https://doi.org/10.47164/ijngc.v15i3.1545
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