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Title	A Survey On Iot Botnets And Their Detection Approaches
ID_Doc	5280
Authors	Kumar Yadav R.; Karamveer K.
Year	2022
Published	Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022
DOI	http://dx.doi.org/10.1109/ICAC3N56670.2022.10074482
Abstract	The Internet consists of multiple interconnected systems/networks, one of which being the Internet of Things (IoT). Despite their flexibility, numerous IoT devices are technically weak in terms of security, making them a prime target for a variety of security breaches, including botnet assaults. IoT applications in the smart city are currently being targeted by advanced persistent threats (APT). Botnets are a piece of malware that permits hackers to take control of several systems and carry out destructive operations. IoT-based botnet assaults have become increasingly common as a result of the development of IoT gadgets, which are more readily hacked than desktop PCs. To combat this new danger, new methods for identifying attacks initiated from infected IoT devices and distinguishing between day and milliseconds duration IoT-based assaults must be developed. The goal of this study was to find, evaluate, and present a comprehensive overview of experimental works on IoT botnet detection research. The detection methods used to detect IoT botnets, the botnet stages, and the botnet stealth strategies were all investigated in this study. The writers examined the nominated study as well as the major approaches used in it. The authors analysed the botnet stages when detection is done and categorised the detection methods depending on the strategies utilized. In this study approach was utilised to provide a core understanding of IoT botnet malware detection techniques The authors examined current research gaps and proposed future research topics as a consequence of this investigation. © 2022 IEEE.
Author Keywords	Artificial neural network; Botnet; Botnet-based assaults; Deep Learning; Internet of Things; IoT; machine learning

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