A Gradient Boosted ML Approach to Feature Selection for Wireless Intrusion Detection

Abstract

The proliferation of Wi-Fi-enabled devices makes security a non-negotiable part of connectivity. As new attacks are discovered that compromise the security of devices in the wireless ecosystem, it is becoming increasingly crucial for intrusion detection systems to generalize to these novel attacks. Machine Learning gives us an approach to do that. In this paper, we provide a feature elimination technique to narrow down the set of features necessary to build such an ML-based solution that takes into account possible class imbalance issues in intrusion datasets. With features extracted using this technique from the AWID dataset, we use a gradient-boosted model to show that these features are necessary to generalize to new attack types in the AWID test dataset.