RESUMEN
The high dimension, complexity, and imbalance of network data are hot issues in the field of intrusion detection. Nowadays, intrusion detection systems face some challenges in improving the accuracy of minority classes detection, detecting unknown attacks, and reducing false alarm rates. To address the above problems, we propose a novel multi-module integrated intrusion detection system, namely GMM-WGAN-IDS. The system consists of three parts, such as feature extraction, imbalance processing, and classification. Firstly, the stacked autoencoder-based feature extraction module (SAE module) is proposed to obtain a deeper representation of the data. Secondly, on the basis of combining the clustering algorithm based on gaussian mixture model and the wasserstein generative adversarial network based on gaussian mixture model, the imbalance processing module (GMM-WGAN) is proposed. Thirdly, the classification module (CNN-LSTM) is designed based on convolutional neural network (CNN) and long short-term memory (LSTM). We evaluate the performance of GMM-WGAN-IDS on the NSL-KDD and UNSW-NB15 datasets, comparing it with other intrusion detection methods. Finally, the experimental results show that our proposed GMM-WGAN-IDS outperforms the state-of-the-art methods and achieves better performance.