Abstract
The Internet of Things (IoT) is a collection of connected computing devices that includes several of our everyday gadgets which allow data to be transferred over the network. The IoT system has its application in various fields including, transportation, smart home, hospitals, smart grid, etc. The ability of devices connected to the web makes them exposed to multiple security intrusions and affects the security traits of the system. Hence, it is vital to investigate intrusion techniques in the IoT context to prevent or identify these intrusions. The primary focus of this review is on intrusion detection systems (IDS) for the IoT system. Therefore, this paper presents a comprehensive review of the latest IDS schemes for the IoT system designed using intelligence techniques, including machine learning, deep learning, and bio-inspired learning. The issues and challenges faced by the IoT-based IDS are presented. Finally, the comparative study and discussion on reviewed IDS scheme are described.
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Singh, R., Ujjwal, R.L. (2023). A Comprehensive Review of IoT-Based IDS Using Intelligence Technique. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 522. Springer, Singapore. https://doi.org/10.1007/978-981-19-5292-0_11
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DOI: https://doi.org/10.1007/978-981-19-5292-0_11
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