An Accurate Ensemble Network with Optimal Architecture for Intrusion Detection Based on Features Representation and Data Augmentation

Cyber security means protecting Internet-connected systems, including hardware, software, and data, against digital threats. In order to increase cyber security, intrusion detection systems (IDS) based on deep models are accepted as the best methods in the literature. However, these methods often face data set limitations. The datasets used are often unbalanced and of insufficient size. In this research, in order to solve these challenges, a precise ensemble network with an optimal architecture for intrusion detection based on features representation and data augmentation has been presented. In the proposed framework, a pre-processing stage is designed to better represent the dataset's features in the recognition process. Also, in the proposed method, data augmentation techniques have been used in the ensemble network training process to solve the challenge of imbalance and inadequacy of the data set. Hyperparameters of the CNN-LSTM model have also been optimized to create an optimal architecture of the ensemble network. The simulation results on the NSLKDD data set show that the proposed intrusion detection system can classify the type of traffic in terms of normality and intrusion with 94% accuracy.