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A Review of IoT Security Solutions Using Machine Learning and Deep Learning

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Proceedings of Data Analytics and Management (ICDAM 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 787))

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Abstract

The Internet of Things (IoT) is a rapidly developing field, projected to connect 22 billion smart devices in a global market worth 1567 billion USD by 2025. The integrated and multidisciplinary nature of these resource-constrained devices responsible for construction of IoT systems renders them susceptible to security attacks. Conventional methods of ensuring security are relatively inefficient as the types, surfaces and severity of attacks continue to evolve. Promising alternatives offered by machine learning (ML) and deep learning (DL) can be employed to embed intelligence in the system, by facilitating the detection of compromised security. In this survey paper, a discussion of IoT infrastructure, security concerns, types and surfaces of attacks prefaces a systematic, layer-wise review of the ML/DL models and frameworks to ensure system security. We also present the current challenges and prospective directions of research concerning the utilization of ML/DL techniques in offering system security in an IoT environment.

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  1. Department of Information Technology, Delhi Technological University, Delhi, India

    Anamika Chauhan & Kapil Sharma

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  1. Anamika Chauhan
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  2. Kapil Sharma
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Correspondence to Anamika Chauhan .

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  1. Department of Information Technology, Bhagwan Parshuram Institute of Technology, New Delhi, Delhi, India

    Abhishek Swaroop

  2. Jan Wyzykowski University, Polkowice, Poland

    Zdzislaw Polkowski

  3. Polytechnic Institute of Portalegre, Portalegre, Portugal

    Sérgio Duarte Correia

  4. Centre for Communications Technology, London Metropolitan university, London, UK

    Bal Virdee

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Chauhan, A., Sharma, K. (2023). A Review of IoT Security Solutions Using Machine Learning and Deep Learning. In: Swaroop, A., Polkowski, Z., Correia, S.D., Virdee, B. (eds) Proceedings of Data Analytics and Management. ICDAM 2023. Lecture Notes in Networks and Systems, vol 787. Springer, Singapore. https://doi.org/10.1007/978-981-99-6550-2_10

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