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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Rajasekhar Chaganti & Azrour Mourade & Vinayakumar Ravi & Naga Vemprala & Amit Dua & Bharat Bhushan, 2022. "A Particle Swarm Optimization and Deep Learning Approach for Intrusion Detection System in Internet of Medical Things," Sustainability, MDPI, vol. 14(19), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12828-:d:936252
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    References listed on IDEAS

    as
    1. Rajasekhar Chaganti & Vijayakumar Varadarajan & Venkata Subbarao Gorantla & Thippa Reddy Gadekallu & Vinayakumar Ravi, 2022. "Blockchain-Based Cloud-Enabled Security Monitoring Using Internet of Things in Smart Agriculture," Future Internet, MDPI, vol. 14(9), pages 1-20, August.
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    Citations

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    Cited by:

    1. Zixuan Ding & Qi Xie, 2023. "Provably Secure Dynamic Anonymous Authentication Protocol for Wireless Sensor Networks in Internet of Things," Sustainability, MDPI, vol. 15(7), pages 1-16, March.
    2. Mohammed Rizwanullah & Hadeel Alsolai & Mohamed K. Nour & Amira Sayed A. Aziz & Mohamed I. Eldesouki & Amgad Atta Abdelmageed, 2023. "Hybrid Muddy Soil Fish Optimization-Based Energy Aware Routing in IoT-Assisted Wireless Sensor Networks," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    3. Firuz Kamalov & Behrouz Pourghebleh & Mehdi Gheisari & Yang Liu & Sherif Moussa, 2023. "Internet of Medical Things Privacy and Security: Challenges, Solutions, and Future Trends from a New Perspective," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    4. Anand Singh Rajawat & S. B. Goyal & Pradeep Bedi & Tony Jan & Md Whaiduzzaman & Mukesh Prasad, 2023. "Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)," Future Internet, MDPI, vol. 15(8), pages 1-21, August.

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