An Ensemble Learning Based Wi-Fi Network Intrusion Detection System (WNIDS)

As the use of Wi-Fi networks grows, so does the increase in security threats. Attackers continue to improve their attack methods, which create the need for developing effective mechanisms to detect the sophisticated attacks. In this work, we propose an implementation of intrusion detection system for Wi-Fi networks using an ensemble learning method. The AWID Wi-Fi intrusion dataset is used to discover the necessary features needed for the efficient IDS implementation. We apply several ensemble learning methods on this dataset and finalize the best one for the proposed IDS implementation. The performance of IDS is reported using well-known metrics including accuracy, precision, recall, and f-measure.