Enhancing Wireless Intrusion Detection Using Machine Learning Classification with Reduced Attribute Sets

In cybersecurity, machine learning approaches can predict and detect threats before they result in major security incidents. The design and performance of an effective machine learning based Intrusion Detection System (IDS) depends upon the selected attributes and the classifier model. This paper considers multi-class classification for the Aegean Wi-Fi Intrusion Dataset (AWID) where classes represent 17 types of the IEEE 802.11 MAC Layer attacks. The proposed work extracts four attribute sets of 32, 10, 7 and 5 attributes, respectfully. The classifiers achieved high accuracy with minimum false positive rates, and the presented work outperforms previous related work in terms of number of classes, attributes and accuracy. The proposed work achieved maximum accuracy of 99.64% for Random Forest with supply test and 99.99% using the 10-fold cross validation approach for Random Forest and J48.