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In this paper, the authors propose a novel hybrid model that efficiently selects the optimal set of features in order to detect 802.11-specific intrusions. Their model for feature selection uses the information gain ratio measure as a means to compute the relevance of each feature and the k-means classifier to select the optimal set of MAC layer features that can improve the accuracy of intrusion detection systems while reducing the learning time of their learning algorithm. In the experimental section of this paper, they study the impact of the optimization of the feature set for wireless intrusion detection systems on the performance and learning time of different types of classifiers based on neural networks.
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