A Method for Network Intrusion Detection Based on GAN-CNN-BiLSTM

Abstract

As network attacks are more and more frequent and network security is more and more serious, it is important to detect network intrusion accurately and efficiently. With the continuous development of deep learning, a lot of research achievements are applied to intrusion detection. Deep learning is more accurate than machine learning, but in the face of a large amount of data learning, the performance will be degraded due to data imbalance. In view of the serious imbalance of network traffic data sets at present, this paper proposes to process data expansion with GAN to solve data imbalance and detect network intrusion in combination with CNN and BiLSTM. In order to verify the efficiency of the model, the CIC-IDS 2017 data set is used for evaluation, and the model is compared with machine learning methods such as Random Forest and Decision Tree. The experiment shows that the performance of this model is significantly improved over other traditional models, and the GAN-CNN-BiLSTM model can improve the efficiency of intrusion detection, and its overall accuracy is improved compared with SVM, DBN, CNN, BiLSTM and other models.