A majority voting technique for Wireless Intrusion Detection Systems

This article aims to build a misuse Wireless Local Area Network Intrusion Detection System (WIDS), and to discover some important fields in WLAN MAC-layer frame to differentiate the attackers from the legitimate devices. We tested several machine-learning algorithms, and found some promising ones to improve the accuracy and computation time on a public dataset. The best performing algorithms that we found are Extra Trees, Random Forests, and Bagging. We then used a majority voting technique to vote on these algorithms. The Bagging classifier and our customized voting technique have good results (about 96.25% and 96.32% respectively) when tested on all the features. We also used a data-mining technique based on Extra Trees ensemble method to find the most important features on Aegean WiFi Intrusion Dataset (AWID) public data-set. After selecting the most 20 important features, Extra Trees and our voting technique were the best performing classifiers in term of accuracy (96.31% and 96.32% respectively).