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Title	Unfolding The Network Dataset To Understand The Contribution Of Features For Detecting Malicious Activities Using Ai/Ml
ID_Doc	59592
Authors	Khatkar M.; Kumar K.; Kumar B.
Year	2022
Published	Materials Today: Proceedings, 59
DOI	http://dx.doi.org/10.1016/j.matpr.2022.04.391
Abstract	As the internet expanded significantly over the last decade, the usage of the Internet of Things (IoT) has helped make smart systems (healthcare, smart cities etc.). However, as IoT networks grow, so does the potential for cyber threats and attacks. In an IoT-based application infrastructure, devices are linked to sensors that connect to big network servers, leaving them exposed to malicious attacks and threats. The current intrusion detection systems (IDS) are not capable to process massive data coming from IoT devices and works only for known intrusion. For such type of smart systems, a smart ML/AI-based IDS system is required that helps process massive data and detect unknown intrusion too. In this paper, we investigate important features from the intrusion dataset which can help to classify malicious activity to develop an efficient smart model. As it is important to understand the dataset well before using it for training any ML/AI classifier model, our goal of this paper is to give a comprehensive view of the popular NSL KDD dataset for finding the best features for developing a robust ML/AI-based IDS for IoT security. Furthermore, this study provides an overview of the most and least used features so that the best feature selection method can be applied in anomaly-based intrusion detection systems in an IoT environment. © 2022
Author Keywords	AI; Features selection; Healthcare; IDS; IoT; Machine learning; Smart cities

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