Robust learning intrusion detection for attacks on wireless networks

Abstract

We address the problem of evaluating the robustness of machine learning based detectors for deployment in real life networks. To this end, we employ Genetic Programming for evolving classifiers and Artificial Neural Networks as our machine learning paradigms under three different Denial-of-Service attacks at the Data Link layer (De-authentication, Authentication and Association attacks). We investigate their cross-platform robustness and cross-attack robustness. Cross-platform robustness is the ability to seamlessly port an Intrusion Detector trained on one network to another network with little or no change and without a drop in performance. Cross-attack robustness is the ability of a detector trained on one attack type to detect a different but similar attack on which it has not been trained. Our results show that the potential of a machine learning based detector can be significantly enhanced or limited by the representation of the training data for the learning algorithms.