Cited By
View all- Boudko S(2023)Federated Learning for Collaborative Cybersecurity of Distributed HealthcareAdvances in Mobile Computing and Multimedia Intelligence10.1007/978-3-031-48348-6_5(57-62)Online publication date: 4-Dec-2023
Intrusion Detection System for IoE-Based Medical Networks
Pages 1 - 18
Abstract
Internet of everything (IoE) has the power of reforming the healthcare sector - various medical devices, hardware, and software applications that are interconnected, tendering a massive volume of data. The huge interconnected medical-based network is prone to significant malicious attacks that can modify the medical data being communicated and transferred. IoE permits dynamic two-way communication and empowers the network with intellect, sophisticated data handling, caching, and allocation mechanisms. In this paper, an improvement in the conventional variable-sized detector generation for healthcare - IVD-IMT algorithm under Artificial Immune System (AIS) based Intrusion Detection System (IDS) capable of handling enormous data generated by the IoE medical network is proposed. Algorithm efficiency is dependent on two performance metrics - detection rate and false alarm rate. The input parameters were tuned using synthetic datasets and then tested over the NSL-KDD dataset. The research lays emphasis on lowering the false alarm rate without compromising on the detection rate.
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Published: 14 April 2023
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View all- Boudko S(2023)Federated Learning for Collaborative Cybersecurity of Distributed HealthcareAdvances in Mobile Computing and Multimedia Intelligence10.1007/978-3-031-48348-6_5(57-62)Online publication date: 4-Dec-2023