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Title	Network Intrusion Detection Leveraging Machine Learning And Feature Selection
ID_Doc	38994
Authors	Ali A.; Shaukat S.; Tayyab M.; Khan M.A.; Khan J.S.; Arshad; Ahmad J.
Year	2020
Published	HONET 2020 - IEEE 17th International Conference on Smart Communities: Improving Quality of Life using ICT, IoT and AI
DOI	http://dx.doi.org/10.1109/HONET50430.2020.9322813
Abstract	Handling superfluous and insignificant features in high-dimension data sets incidents led to a long-term demand for system anomaly detection. Ignoring such elements with spectral instruction not speeds up the analysis process but again facilitates classifiers to make accurate selections during attack perception stage, when wrestling with huge-scale and heterogeneous data. In this paper, for dimensionality reduction of data, we use Correlation-based Feature Selection (CFS) and Naïve Bayes (NB) classifier techniques. The proposed Intrusion Detection System (IDS) classifies attacks using a Multilayer Perceptron (MLP) and Instance-Based Learning algorithm (IBK). The accuracy of the introduced IDS is 99.87% and 99.82% with only 5 and 3 features out of 78 features for IBK. Other metrics such as precision, Recall, F-measure, and Receiver Operating Curve (ROC) also confirm the principal performance of IBK compared to MLP. © 2020 IEEE.
Author Keywords	Classifier subset evaluation; Correlation-Based Feature (CFS); Instance-Based Learning algorithm (IBK); Intrusion Detection System (IDS); Multilayer Perceptron (MLP)

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