| Autor: |
M. Akshay Kumaar, Duraimurugan Samiayya, P. M. Durai Raj Vincent, Kathiravan Srinivasan, Chuan-Yu Chang, Harish Ganesh |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Frontiers in Public Health, Vol 9 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2296-2565 |
| DOI: |
10.3389/fpubh.2021.824898 |
| Popis: |
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 Most of the existing artificial intelligence-based systems are trained on outdated intrusion detection repositories, which can produce more false positives and require retraining the algorithm from scratch to support new attacks.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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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