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Title	A Survey: An Effective Utilization Of Machine Learning Algorithms In Iot Based Intrusion Detection System
ID_Doc	5365
Authors	Natarajan B.; Bose S.; Maheswaran N.; Logeswari G.; Anitha T.
Year	2023
Published	12th IEEE International Conference on Advanced Computing, ICoAC 2023
DOI	http://dx.doi.org/10.1109/ICoAC59537.2023.10249672
Abstract	With tremendous advancements in technologies, the life has become easier while on other end, security issues pose a serious threat. Cyberattacks have increased by multiple folds due to drastic evolution of internet in the past decade. In this background, Intrusion Detection System (IDS) is one of the protective layers that safeguard the interests of internet consumers. IDS offer a safe zone for consumers in general and company owners so they may go about their daily business uninterrupted by malicious network activity. In recent times, Machine Learning (ML) algorithms are increasingly applied to find and categorize the security threats. The current research article conducts a comparison of EffectivE Utilization of Machine Learning Algorithms(EUMLA) implemented in IDS so far, for various applications like IoT, cloud services, big data, 5G network and smart cities. Further, the current work also aims at intrusion classification with the help of ML algorithms such as Classification and Regression Trees (CART), LDA (Linear Discriminant Analysis) and Random Forest algorithms. The KDD-CUP dataset was used to validate the proposed model, and its performance was analyzed and compared to other methodologies. © 2023 IEEE.
Author Keywords	Classification; Intrusion Detection System (IDS) and Internet of Things

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