Combination of Feature Selection and Hybrid Classifier as to Network Intrusion Detection System Adopting FA, GWO, and BAT Optimizers

Abstract

In terms of network topology, one of the extensively utilized technologies is the intrusion detection system (IDS). Despite applying numerous machine learning approaches (supervised and unsupervised) to enhance efficacy, reaching high-grade performance is still a challenging problem for existing intrusion detection algorithms. This study presents a new technique for IDS that focuses on various deep neural networks (DNNs) and their combination for data classification. The proposed model consists of three parts: (1) the feature selection is composed of an intersection of mutual information based on the transductive model (MIT-MIT), Anova F-value, and Genetic Algorithm (GA) methods, (2) the second section is a classifier network using a hybrid CNN-LSTM algorithm, and (3) the hyperparameter optimization module that puts to use Firefly, BAT, and Gray Wolf algorithms. In order to validate and verify the suggested model via accuracy, F1 score, recall, and precision criteria, a benchmark dataset, namely, NSL-KDD, is employed, which compares the proposed method with the highly developed classifiers. The comparison outcomes confirmed the surpassing of the presented strategy over contrast algorithms.