Meta-Fed IDS: Meta-learning and Federated learning based fog-cloud approach to detect known and zero-day cyber attacks in IoMT networks
The Internet of Medical Things (IoMT) is a transformative fusion of medical sensors, equipment, and the Internet of Things, positioned to transform healthcare. However, security and privacy concerns hinder widespread IoMT adoption, intensified by the scarcity of high-quality datasets for developing...
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Veröffentlicht in: | Journal of parallel and distributed computing 2024-10, Vol.192, p.104934, Article 104934 |
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Sprache: | eng |
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Zusammenfassung: | The Internet of Medical Things (IoMT) is a transformative fusion of medical sensors, equipment, and the Internet of Things, positioned to transform healthcare. However, security and privacy concerns hinder widespread IoMT adoption, intensified by the scarcity of high-quality datasets for developing effective security solutions. Addressing these challenges, we propose a novel framework for cyberattack detection in dynamic IoMT networks. This framework integrates Federated Learning with Meta-learning, employing a multi-phase architecture for identifying known attacks, and incorporates advanced clustering and biased classifiers to address zero-day attacks. The framework's deployment is adaptable to dynamic and diverse environments, utilizing an Infrastructure-as-a-Service (IaaS) model on the cloud and a Software-as-a-Service (SaaS) model on the fog end. To reflect real-world scenarios, we introduce a specialized IoMT dataset. Our experimental results indicate high accuracy and low misclassification rates, demonstrating the framework's capability in detecting cyber threats in complex IoMT environments. This approach shows significant promise in bolstering cybersecurity in advanced healthcare technologies.
•We introduce Meta-Fed IDS, for detection of known cyber attacks.•We propose Anoml-Fed IDS, to effectively detect the zero-day cyber attacks.•We recommend the creation of m-IoT-env dataset.•We suggest a novel IDS deployment: IaaS on cloud and SaaS on fog nodes.•Our experimental results validate the efficacy of our proposed approach. |
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ISSN: | 0743-7315 1096-0848 |
DOI: | 10.1016/j.jpdc.2024.104934 |