Application of a New Feature Generation Algorithm in Intrusion Detection System

The intrusion detection system is designed to discover the abnormal behavior of the network system, but it has the problems of low detection accuracy, inability to perform fine detection, and huge time cost. Therefore, it is necessary to design a fast and accurate intrusion detection system. Therefore, this paper proposes a multigranularity feature generation+XGBOOST method to improve the intrusion detection system. First, we propose a multigranularity feature generation algorithm, which converts all features into discrete features with different numbers of categories. Different numbers of categories represent different granularities. We believe that the combination of multiple different granular features can achieve better accurate attack detection. Then, we use the proposed method to perform experimental verification on the four datasets of KDD99, NSL-KDD, UNSW_NB15, and CSE-CIC-IDS2018. For the KDD99 dataset, detection rates of 100%, 100%, and 99.43% can be achieved in the two-category, five-category, and multicategory tasks, respectively; for the NSL-KDD dataset, detection rates of 100%, 100%, and 90.84% can be achieved in the two-category, five-category, and multicategory tasks, respectively; for the UNSW_NB15 dataset, 100% detection rate can be achieved in the second and tenth categories; for the CSE-CIC-IDS2018 dataset, 100% detection rate can be achieved in the third classification. Experiments show that the proposed algorithm can achieve accurate and precise detection. Finally, we experiment with the multigranularity feature generation algorithm on multiple classifiers and multiple datasets to prove the generalization ability of the proposed feature generation algorithm and compare the proposed algorithm with the CFS algorithm to prove the efficiency of the algorithm.