A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks

Abstract

An intrusion detection system, often known as an IDS, is extremely important for preventing attacks on a network, violating network policies, and gaining unauthorized access to a network. The effectiveness of IDS is highly dependent on data preprocessing techniques and classification models used to enhance accuracy and reduce model training and testing time. For the purpose of anomaly identification, researchers have developed several machine learning and deep learning-based algorithms; nonetheless, accurate anomaly detection with low test and train times remains a challenge. Using a hybrid feature selection approach and a deep neural network- (DNN-) based classifier, the authors of this research suggest an enhanced intrusion detection system (IDS). In order to construct a subset of reduced and optimal features that may be used for classification, a hybrid feature selection model that consists of three methods, namely, chi square, ANOVA, and principal component analysis (PCA), is applied. These methods are referred to as “the big three.” On the NSL-KDD dataset, the suggested model receives training and is then evaluated. The proposed method was successful in achieving the following results: a reduction of input data by 40%, an average accuracy of 99.73%, a precision score of 99.75%, an F1 score of 99.72%, and an average training and testing time of 138% and 2.7 seconds, respectively. The findings of the experiments demonstrate that the proposed model is superior to the performance of the other comparison approaches.