Hybrid Deep Learning Network Intrusion Detection System Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory

Network security has become crucial in an era where information and data are valuable assets. An effective Network Intrusion Detection System (NIDS) is required to protect sensitive data and information from cyberattacks. Numerous studies have created NIDS using machine learning algorithms and network datasets that do not accurately reflect actual network data flows. Increasing hardware capabilities and the ability to process big data have made deep learning the preferred method for developing NIDS. This study develops a NIDS model using two deep learning algorithms: Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (BiLSTM). CNN extracts spatial features in the proposed model, while BiLSTM extracts temporal features. Two publicly available benchmark datasets, CICIDS2017 and UNSW-NB15, are used to evaluate the model. The proposed model surpasses the previous method in terms of accuracy, achieving 99.83% and 99.81% for binary and multiclass classification on the CICIDS2017 dataset. On the UNSW-NB15 dataset, the model achieves accuracies of 94.22% and 82.91% for binary and multiclass classification, respectively. Moreover, Principal Component Analysis (PCA) is also used for feature engineering to improve the speed of model training and reduce existing features to ten dimensions without significantly impacting the model's performance.