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

Rajasekhar Chaganti (), Azrour Mourade, Vinayakumar Ravi, Naga Vemprala, Amit Dua and Bharat Bhushan
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Rajasekhar Chaganti: Toyota Research Institute, Los Altos, CA 94022, USA
Azrour Mourade: Computer Sciences Department, Faculty of Sciences and Technics, Moulay Ismail University, Meknes 50050, Morocco
Vinayakumar Ravi: Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Al-Khober 34754, Saudi Arabia
Naga Vemprala: Pamplin School of Business, University of Portland, Portland, OR 97203, USA
Amit Dua: Department of Algorithmics and Software, Silesian University of Technology, 44-100 Gliwice, Poland
Bharat Bhushan: Department of Computer Science and Engineering, School of Engineering and Technology (SET), Sharda University, Greater Noida, Uttar Pradesh 201310, India

Sustainability, 2022, vol. 14, issue 19, 1-18

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.

Keywords: internet of medical things; cyber security; intrusion detection system; particle swarm optimization; deep learning; deep neural network; network attacks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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