A Feature Selection Algorithm to Find Optimal Feature Subsets for Detecting DoS Attacks

The performance of network intrusion detection systems based on machine learning techniques largely depends on the selected features. However, choosing the optimal subset of features from a given feature set requires extensive computing resources. To tackle this problem we propose an optimal feature selection algorithm based on a local search algorithm. In order to evaluate the performance of our proposed algorithm, comparisons with a feature set composed of all 41 features are carried out over the NSL-KDD data set using a multi-layer perceptron.