A majority voting technique for Wireless Intrusion Detection Systems

This poster 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 Bagging classifier and our customized voting technique have good results (about 96.25% and 96.32% respectively) when tested on all the features.