Abstract:

In the field of intrusion detection,redundancy and extraneous features not only slow down the classification process,but also prevent the classifier from making accurate decisions,resulting in intrusion detection system performance degradation.A network intrusion detection system based on artificial fish feature selection is proposed to address the problem of low system accuracy induced by high-dimensional data sets in intrusion detection.First,the original data set is preprocessed,with the data cleaned and standardized.Then,an improved multi-objective artificial fish swarm algorithm(AFSA) is presented by merging the adaptive parameter modifications and the multi-objective optimization algorithm.By dynamically optimizing the search space,the search ability is improved,and the optimal feature subset is selected.Finally,an intrusion detection model is established based on a genetic algorithm and CatBoost improved multi-objective artificial fish swarm optimization approach.The generated multi-feature subsets are classified by CatBoost for feature evaluation,and the effectiveness of feature selection is tested.The proposed feature selection approach employs 17-dimensional features to achieve an accuracy of 93.97% on the NSL-KDD dataset,while it uses 24-dimensional features to achieve an accuracy of 95.06% on the UNSW-NB15 dataset.Simulation results show that the proposed algorithm can achieve a high accuracy while having a low dimension,which has certain advantages compared with existing feature selection methods.

Key words: intrusion detection system, feature selection, artificial fish swarm algorithm, multi-objective optimization