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A Particle Swarm Optimization and Deep Learning Approach for Intrusion Detection System in Internet of Medical Things

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dc.contributor.author Dua, Amit
dc.date.accessioned 2024-10-07T12:12:49Z
dc.date.available 2024-10-07T12:12:49Z
dc.date.issued 2022-10
dc.identifier.uri https://www.mdpi.com/2071-1050/14/19/12828
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16044
dc.description.abstract Integrating the internet of things (IoT) in medical applications has significantly improved healthcare operations and patient treatment activities. Real-time patient monitoring and remote diagnostics allow the physician to serve more patients and save human lives using internet of medical things (IoMT) technology. However, IoMT devices are prone to cyber attacks, and security and privacy have been a concern. The IoMT devices operate on low computing and low memory, and implementing security technology on IoMT devices is not feasible. In this article, we propose particle swarm optimization deep neural network (PSO-DNN) for implementing an effective and accurate intrusion detection system in IoMT. Our approach outperforms the state of the art with an accuracy of 96% to detect network intrusions using the combined network traffic and patient’s sensing dataset. We also present an extensive analysis of using various Machine Learning(ML) and Deep Learning (DL) techniques for network intrusion detection in IoMT and confirm that DL models perform slightly better than ML models. en_US
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.subject Computer Science en_US
dc.subject Internet of medical things en_US
dc.subject Cyber security en_US
dc.subject Intrusion detection system en_US
dc.subject Deep neural network en_US
dc.subject Network attacks en_US
dc.title A Particle Swarm Optimization and Deep Learning Approach for Intrusion Detection System in Internet of Medical Things en_US
dc.type Article en_US


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