IDS-PSO-BAE: The Ensemble Method for Intrusion Detection System Using Bagging–Autoencoder and PSO

In recent days, for security services, an intrusion detection system (IDS) is a highly effective solution (Louk MHL, Tama BA. “PSO-Driven Feature Selection and Hybrid Ensemble for Network Anomaly Detection,” Big Data and Cognitive Computing, 2022; Chebrolu et al. in Comput Secur 24:295–307, 2005). The primary goal of IDSs is to facilitate the detection of sophisticated network attacks by empowering protection instruments. Multiple ML/DL algorithms have been proposed for IDS (Choi H, Kim M, Lee G, Kim W. Unsupervised learning approach for network intrusion detection system using autoencoders. The Journal of Supercomputing, 2019). In this work, proposed IDS-PSO-BAE, an ensemble framework to improve the performance of the IDS using PSO-based feature selection, and bagging-based autoencoder classification. The hyperparameter settings of the autoencoders result in the best detection performance, and this method is good for unknown types of attacks’ detection. With bagging, a weak learner can be transformed into an effective learner, enabling precise classification. The best set of features to serve into the ensemble model is selected using PSO-enabled feature selection. The study, in which the complete train dataset is split into set of sub data sets and applied autoencoder on individual intrusion subset with specific ensemble learning. At the end, all results of individual ensemble learning are combined as final class prediction with the voting technique. The final feature subsets from the NSL-KDD dataset (Dhanabal and Shantharajah in Int. J. Adv. Res. Comput. Commun. Eng. 4–6:446–452, 2015) are trained with a hybrid ensemble learner for IDS. The results of the IDS-PSO-BAE model have superior accuracy, recall, and F-score compared to standard methods.