Abstract
The Internet of Things (IoT), which incorporates different devices into the networks to give sophisticated and intellectual services, needs to ensure client security and deal with attacks for example denial of service, eavesdropping, spoofing attacks and jamming. Network layer attacks on IoT can cause huge disturbances and loss of data. Then again, the crosscutting idea of IoT frameworks and the multidisciplinary parts engaged with the deployment of such frameworks have presented new security challenges. We examine the variety of attack models for IoT framework and address the security challenges and solutions based on deep learning and machine learning techniques. This paper provides a wide review of challenges and research opportunities that concerned in applying by ML/DL.
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Velliangiri, S., Kasaraneni, K.K. (2020). Machine Learning and Deep Learning in Cyber Security for IoT. In: Kumar, A., Paprzycki, M., Gunjan, V. (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_107
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DOI: https://doi.org/10.1007/978-981-15-1420-3_107
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