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Title	Intrusion Detection Framework In Iot Networks
ID_Doc	33331
Authors	Bajpai S.; Sharma K.; Chaurasia B.K.
Year	2023
Published	SN Computer Science, 4, 4
DOI	http://dx.doi.org/10.1007/s42979-023-01770-9
Abstract	The Internet of Things (IoT) deals with the internet and physical objects such as smart home automation, industrial applications, smart cities, health and fitness, environmental monitoring, etc. The IoT network is interconnected over the internet for data analysis and sharing. It has made it possible for people and organizations to accomplish more with less, both in terms of time and resources. However, this accomplishment of the IoT has also contributed to a worrying increase in IoT network threats. Botnet invasions are likely the most worrisome of these assaults. Attackers are also becoming more inventive as time and technology develop. To recognize these assaults and detect these incursions before they cause the system to become unresponsive, it is crucial to deploy stronger and more efficient machine learning technologies. By employing supervised learning and evaluating its overall performance, this work aims to design an intrusion detection framework in IoT networks using machine learning (IDFML). The proposed IDFML will examine the various performance measures for each of the different classifiers. Results show that the proposed IDFML has a 98.68% attack detection accuracy. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Author Keywords	Anomaly detection; Internet of Things (IoT); Intrusion detection system (IDS); IoT20IDS; Machine learning (ML)

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