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Implementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes

Author

Listed:
  • Abbas Javed

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Punjab 54000, Pakistan)

  • Amna Ehtsham

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Punjab 54000, Pakistan)

  • Muhammad Jawad

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Punjab 54000, Pakistan
    Hitachi Energy Research, Pawia 7, 31-154 Kraków, Poland)

  • Muhammad Naeem Awais

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Punjab 54000, Pakistan)

  • Ayyaz-ul-Haq Qureshi

    (Department of Cyber Security and Networks, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

  • Hadi Larijani

    (SMART Technology Research Centre, Department of Cyber Security and Networks, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

Abstract

Smart home devices, also known as IoT devices, provide significant convenience; however, they also present opportunities for attackers to jeopardize homeowners’ security and privacy. Securing these IoT devices is a formidable challenge because of their limited computational resources. Machine learning-based intrusion detection systems (IDSs) have been implemented on the edge and the cloud; however, IDSs have not been embedded in IoT devices. To address this, we propose a novel machine learning-based two-layered IDS for smart home IoT devices, enhancing accuracy and computational efficiency. The first layer of the proposed IDS is deployed on a microcontroller-based smart thermostat, which uploads the data to a website hosted on a cloud server. The second layer of the IDS is deployed on the cloud side for classification of attacks. The proposed IDS can detect the threats with an accuracy of 99.50% at cloud level (multiclassification). For real-time testing, we implemented the Raspberry Pi 4-based adversary to generate a dataset for man-in-the-middle (MITM) and denial of service (DoS) attacks on smart thermostats. The results show that the XGBoost-based IDS detects MITM and DoS attacks in 3.51 ms on a smart thermostat with an accuracy of 97.59%.

Suggested Citation

  • Abbas Javed & Amna Ehtsham & Muhammad Jawad & Muhammad Naeem Awais & Ayyaz-ul-Haq Qureshi & Hadi Larijani, 2024. "Implementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes," Future Internet, MDPI, vol. 16(6), pages 1-22, June.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:6:p:200-:d:1408890
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    References listed on IDEAS

    as
    1. Vikas Tomer & Sachin Sharma, 2022. "Detecting IoT Attacks Using an Ensemble Machine Learning Model," Future Internet, MDPI, vol. 14(4), pages 1-17, March.
    2. Sheeraz Ahmed & Zahoor Ali Khan & Syed Muhammad Mohsin & Shahid Latif & Sheraz Aslam & Hana Mujlid & Muhammad Adil & Zeeshan Najam, 2023. "Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron," Future Internet, MDPI, vol. 15(2), pages 1-24, February.
    3. Ashish Koirala & Rabindra Bista & Joao C. Ferreira, 2023. "Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms," Future Internet, MDPI, vol. 15(6), pages 1-30, June.
    Full references (including those not matched with items on IDEAS)

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