The regular discovery of new attacks on 802.11 wireless devices emphasizes the importance of developing ML-based intrusion detection systems that generalize to such attacks. Class-imbalance issues in intrusion datasets pose a learning problem for ML solutions. Our methodology directly incorporates the class-imbalance issues to learn better feature importance measures. 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.
A Gradient Boosted ML Approach to Feature Selection for Wireless Intrusion Detection
01.06.2023
1073007 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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