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Energy Analysis-Based Cyber Attack Detection by IoT with Artificial Intelligence in a Sustainable Smart City

Author

Listed:
  • D. Prabakar

    (Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai 603203, India)

  • M. Sundarrajan

    (Department of CSE, SRM Institute of Science and Technology, Ramapuram Campus, Chennai 600089, India)

  • R. Manikandan

    (School of Computing, SASTRA Deemed University, Thanjavur 613401, India)

  • N. Z. Jhanjhi

    (School of Computer Science, SCS, Taylor’s University, Subang Jaya 47500, Malaysia)

  • Mehedi Masud

    (Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia)

  • Abdulmajeed Alqhatani

    (Department of Information Systems, College of Computer Science & Information Systems, Najran University, Najran 61441, Saudi Arabia)

Abstract

Cybersecurity continues to be a major issue for all industries engaged in digital activity given the cyclical surge in security incidents. Since more Internet of Things (IoT) devices are being used in homes, offices, transportation, healthcare, and other venues, malicious attacks are happening more frequently. Since distance between IoT as well as fog devices is closer than distance between IoT devices as well as the cloud, attacks can be quickly detected by integrating fog computing into IoT. Due to the vast amount of data produced by IoT devices, ML is commonly employed for attack detection. This research proposes novel technique in cybersecurity-based network traffic analysis and malicious attack detection using IoT artificial intelligence techniques for a sustainable smart city. A traffic analysis has been carried out using a kernel quadratic vector discriminant machine which enhances the data transmission by reducing network traffic. This enhances energy efficiency with reduced traffic. Then, the malicious attack detection is carried out using adversarial Bayesian belief networks. The experimental analysis has been carried out in terms of throughput, data traffic analysis, end-end delay, packet delivery ratio, energy efficiency, and QoS. The proposed technique attained a throughput of 98%, data traffic analysis of 74%, end-end delay of 45%, packet delivery ratio of 92%, energy efficiency of 92%, and QoS of 79%.

Suggested Citation

  • D. Prabakar & M. Sundarrajan & R. Manikandan & N. Z. Jhanjhi & Mehedi Masud & Abdulmajeed Alqhatani, 2023. "Energy Analysis-Based Cyber Attack Detection by IoT with Artificial Intelligence in a Sustainable Smart City," Sustainability, MDPI, vol. 15(7), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6031-:d:1112210
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    References listed on IDEAS

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    1. Gao, Zheming & Fang, Shu-Cherng & Luo, Jian & Medhin, Negash, 2021. "A kernel-free double well potential support vector machine with applications," European Journal of Operational Research, Elsevier, vol. 290(1), pages 248-262.
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    Cited by:

    1. Alejandro Valencia-Arias & Juana Ramírez Dávila & Wilmer Londoño-Celis & Lucia Palacios-Moya & Julio Leyrer Hernández & Erica Agudelo-Ceballos & Hernán Uribe-Bedoya, 2024. "Research Trends in the Use of the Internet of Things in Sustainability Practices: A Systematic Review," Sustainability, MDPI, vol. 16(7), pages 1-23, March.

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