Firstly, we calculate the feature importance or ranks based on individual methods, then add features to subsets sequentially by importance and evaluate subset performance comprehensively by designing an NSOM to obtain the subset with the largest NSOM value. This work designs an ensemble-based automatic feature selection method called EAFS. In contrast, wrapped feature selection methods are computationally complex and time-consuming meanwhile, individual feature selection methods have a bias in evaluating features. Most current filtered and embedded feature selection methods use a fixed threshold or ratio to determine the number of features in a subset, which requires a priori knowledge. The mass of redundant and irrelevant data in network traffic brings serious challenges to intrusion detection, and feature selection can effectively remove meaningless information from the data. The obtained results are compared with other feature selection approaches to verify the efficiency of our proposed approach. Three different decision tree classifiers are used to measure the performance of the selected subsets of features. The experiment will be conducted on the KDD99 dataset and the UNSW-NB15 dataset. In this work, we apply a wrapper approach based on a genetic algorithm as a search strategy and logistic regression as a learning algorithm for network intrusion detection systems to select the best subset of features. Feature selection is needed to select the optimal subset of features that represents the entire dataset to increase the accuracy and the classification performance of the IDS.

Due to the high dimensionality of the exchanged data, applying those techniques will be extremely time consuming. Different IDS approaches have emerged using data mining, machine learning, statistical analysis, and artificial intelligence techniques such as genetic algorithms, artificial neural networks, fuzzy logic, swarm intelligence, etc. Intrusion detection systems (IDSs) have been developed to monitor and filter network activities by identifying attacks and alerting network administrators. Through malicious actions, hackers can have unauthorised access that compromises the integrity, the confidentiality, and the availability of resources or services.

Intrusions constitute one of the main issues in computer network security.

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