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Title	Ml Techniques For Attack And Anomaly Detection In Internet Of Things Networks
ID_Doc	37192
Authors	Mahor V.; Bijrothiya S.; Mishra R.; Rawat R.
Year	2022
Published	Autonomous Vehicles, 1
DOI	http://dx.doi.org/10.1002/9781119871989.ch13
Abstract	The Internet of Things (IoT), is a fundamental driver of smart cities. It is the champion of the global collaboration of machines/things, people, huge data, and processes to create cities that are efficient, economically feasible, and human-friendly. It is a technique that connects thousands of autonomous devices to accumulate different types of data and their circumstances via external devices. With this they can share the information with authorized personnel to serve various purposes including organizing and coordinating economic services and developing commercial services and activities. On the other side, the Internet of Things is currently experiencing more security challenges than ever before. Machine Learning (ML) has made major technological progress, offering up a slew of new scientific avenues to solve existing and future IoT concerns. It is an effective technique for identifying dangers and suspicious acts carried out by sophisticated gadgets and networking in clever wired and wireless networks. This article is mainly focused on ML approaches and the relevance of IoT security in the framework of different types of probable assaults. Alternative ML-based IoT security approaches have also been suggested. © 2023 Scrivener Publishing LLC.
Author Keywords	Anomaly-detection; Cyber-attacks; Internet of Things (IoT); Machine Learning (ML)

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