Abstract
In recent years, the Internet of Things has grown visibly and soon will be an essential part of our daily lives. This increases the number of transactions in a network and with that risks to these sensitive data increase too; thus, we require a smart system to detect any unauthorized advances to an IoT network and prevent those risks. This system predicts and delivers possibilities of the intrusion based on a few attributes identified using feature engineering. An Intrusion Detection System is tested on its ability to detect malicious activities within IoT networks. Here, we propose an Anomaly-based Intrusion Detection System that detects and prevents attacks on the IoT environment. This approach has two primary objectives to address. First, the data require filtration using the correlation coefficient to combine the probability of distribution to identify features that have a positive impact on the accuracy. Second, the classifier algorithm identifies the behavior using the trust factor based on the selected features. In this step, we analyze the precision, recall, and f1-score of the model on a pre-existing NSL-KDD dataset where the proposed model obtained 98.4% accuracy along with high TPR (True-Positive Rate) and low FPR (False-Positive Rate).
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This article is part of the topical collection “Enabling Innovative Computational Intelligence Technologies for IOT” guest edited by Omer Rana, Rajiv Misra, Alexander Pfeiffer, Luigi Troiano, and Nishtha Kesswani.
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Singh, K.P., Kesswani, N. An Anomaly-Based Intrusion Detection System for IoT Networks Using Trust Factor. SN COMPUT. SCI. 3, 168 (2022). https://doi.org/10.1007/s42979-022-01053-9
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DOI: https://doi.org/10.1007/s42979-022-01053-9