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Title	Comparative Analysis Of Intrusion Detection Schemes In Internet Of Things(Iot) Based Applications
ID_Doc	14994
Authors	El Zegil F.; Nanda P.; Mohanty M.; Alzahrani M.
Year	2024
Published	2024 17th International Conference on Security of Information and Networks, SIN 2024
DOI	http://dx.doi.org/10.1109/SIN63213.2024.10871905
Abstract	Due to massive growth in IoT devices in recent years, security of these devices is a major concern. IoT applications span across a number of fields including but not limited to smart cities, intelligent agriculture systems, and the innovative industry. Despite its benefits, cybersecurity challenges have increased significantly in IoT environments. The lack of resource capacity and sophisticated security measurement exposes IoT devices to large number of recent attacks. A strong intrusion detection system (IDS) is the best way to secure IoT devices. Various studies have shown that the current IDS fails to detect modern malware in IoT environments. Some datasets do not have complex scenarios of attack. There are limitations of current datasets, or heterogeneous data of the IoT environment, such as KDD99, NLS_KDD, and UNSW _NB15. In addition, these datasets do not include an operating system and network monitoring audits. This paper comprehensively compares five machine-learning models on the recent EDGE-IIoT dataset. We examine these machine learning methods on Binary and Multiclass class IDS and the security challenges in managing current and future attacks in an IoT environment. © 2024 IEEE.
Author Keywords	Edge-IIoT dataset; Internet of Things; Intrusion Detection System; IoT; Machine Learning; NIDS

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