Abstract
IoT is an emerging technology, which represents a complex and heterogeneous environment. Thus, security in IoT could be an issue of concern, in particular detecting and identifying malicious events. Malicious events are triggered when anomalous traffic attempts to threaten and abuse the IoT network. Machine learning approaches provide interesting tools to detect new attacks and prevent unauthorized access. Therefore, the aim of this paper is to investigate and compare the performances of the classical machine learning methods: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and K-means. The performance metrics considered in this study are Accuracy, Detection Rate, False Alarm Rate, Recall, Precision, F1- Score, Time Training and Time Assigned Label. Then, a proposed solution for enhancement is elaborated by leveraging the multi-level tweak. The proposed solution shows the best performance results compared to classical machine learning methods for intrusion detection.
Keywords
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Mliki, H., Kaceam, A.H., Chaari, L. (2020). Intrusion Detection Study and Enhancement Using Machine Learning. In: Kallel, S., Cuppens, F., Cuppens-Boulahia, N., Hadj Kacem, A. (eds) Risks and Security of Internet and Systems. CRiSIS 2019. Lecture Notes in Computer Science(), vol 12026. Springer, Cham. https://doi.org/10.1007/978-3-030-41568-6_17
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