Improved Grey Wolf Optimization- (IGWO-) Based Feature Selection on Multiview Features and Enhanced Multimodal-Sequential Network Intrusion Detection Approach

Abstract

The goal of the network intrusion detection system (NIDS) is to spot malicious activity in a network. It seeks to do that by examining the behavior of the traffic network. To find abnormalities, the NIDS heavily use machine learning (ML) and data mining techniques. The performance of NIDSs is significantly impacted by feature selection. This is due to the numerous characteristics that are used in anomaly identification, which take a lot of time. The time required to analyze traffic behavior and raise the accuracy level is thus influenced by the feature selection strategy. In the current work, the researcher’s goal was to provide a feature selection model for NIDSs. IGWO (improved grey wolf optimizations) for FSs (feature selections) was proposed to address these difficulties. The three primary processes in this proposed study are preprocessing, extractions and classifications of FSs, and evaluations of results. IGWOs are used to choose a subset of input variables by minimizing features to measure the accuracy in the search space and discover the best solution. A particular structure of HPNs (hierarchical progressive networks) is controlled by the MDAEs (multimodal deep autoencoders) and ABLSTMs (attention-based long short-term memories) for enhanced multimodal-sequential IDSs, i.e., AB-LSTMs. It is possible to understand relationships between neighboring network connections automatically and efficiently integrate information from many levels of characteristics inside a network connection using the EMS-DHPN technique simultaneously. This work’s suggested hybrid IDSs called IGWO-EMS-DHPN technique were evaluated using two intrusion datasets: UNSW-NB15 and CICIDS-2017 which is compared with other existing classifiers in terms of relative accuracies, precisions, recalls, and F 1 -scores in categorizations. While several classifiers have been developed, the suggested IGWO-EMS-DHPN classifier obtains maximum accuracy.