A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning

Author:

Akshay Kumaar M.,Samiayya Duraimurugan,Vincent P. M. Durai Raj,Srinivasan Kathiravan,Chang Chuan-Yu,Ganesh Harish

Abstract

The unbounded increase in network traffic and user data has made it difficult for network intrusion detection systems to be abreast and perform well. Intrusion Systems are crucial in e-healthcare since the patients' medical records should be kept highly secure, confidential, and accurate. Any change in the actual patient data can lead to errors in the diagnosis and treatment. Most of the existing artificial intelligence-based systems are trained on outdated intrusion detection repositories, which can produce more false positives and require retraining the algorithm from scratch to support new attacks. These processes also make it challenging to secure patient records in medical systems as the intrusion detection mechanisms can become frequently obsolete. This paper proposes a hybrid framework using Deep Learning named “ImmuneNet” to recognize the latest intrusion attacks and defend healthcare data. The proposed framework uses multiple feature engineering processes, oversampling methods to improve class balance, and hyper-parameter optimization techniques to achieve high accuracy and performance. The architecture contains <1 million parameters, making it lightweight, fast, and IoT-friendly, suitable for deploying the IDS on medical devices and healthcare systems. The performance of ImmuneNet was benchmarked against several other machine learning algorithms on the Canadian Institute for Cybersecurity's Intrusion Detection System 2017, 2018, and Bell DNS 2021 datasets which contain extensive real-time and latest cyber attack data. Out of all the experiments, ImmuneNet performed the best on the CIC Bell DNS 2021 dataset with about 99.19% accuracy, 99.22% precision, 99.19% recall, and 99.2% ROC-AUC scores, which are comparatively better and up-to-date than other existing approaches in classifying between requests that are normal, intrusion, and other cyber attacks.

Funder

Ministry of Education

Ministry of Science and Technology, Taiwan

Publisher

Frontiers Media SA

Subject

Public Health, Environmental and Occupational Health

Cited by 40 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep Learning for Enhanced IoMT Security: A GNN-BiLSTM Intrusion Detection System;2024 International Conference on Circuit, Systems and Communication (ICCSC);2024-06-28

2. Towards a Federated Intrusion Detection System based on Neuromorphic Computing;2024 9th International Conference on Smart and Sustainable Technologies (SpliTech);2024-06-25

3. Security Analysis for Smart Healthcare Systems;Sensors;2024-05-24

4. A Novel Framework for Securing Information in Cyber Security System Using Authentication, Intrusion Detection and Deep Learning-Based Risk Prediction Tasks;2024 5th International Conference for Emerging Technology (INCET);2024-05-24

5. Predicting heart disease based on an intelligent healthcare monitoring system using HPM-NIA;Multimedia Tools and Applications;2024-05-22

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