Abstract
Internet of things is enabling the world to become smarter and more amendable, and it connects the digital and physical worlds together. The Internet of things relies on resource-limited devices ranging from domestic appliances to personal devices. There is an ever-increasing number of device connected to the Internet every day that attract the attention of hackers. Adversaries deploy a range of advanced persistent threat (APT) strategies to successfully compromise these systems, one of which is the botnet attack. The IoT is potentially vulnerable to attacks launched by intelligent botnets, since these botnets detect network weaknesses and exploit them for the launch of different attacks similar to DDoS attacks. In order to provide efficient security against botnet attack to the IoT devices and network, several methods have emerged; recent and most effective mechanism is a deep learning mechanism. Our paper reviewed the security threats in IoT and several existing deep learning approaches to address the detection of botnets in the IoT environment. Furthermore, we also investigated the attack class, datasets, merits and demerits of existing deep learning approaches.
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Sakthipriya, N., Govindasamy, V., Akila, V. (2023). Review of Deep Learning Approaches for IoT Botnet Detection. In: Kumar, S., Hiranwal, S., Purohit, S.D., Prasad, M. (eds) Proceedings of International Conference on Communication and Computational Technologies . Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-3951-8_40
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