TGA: A Novel Network Intrusion Detection Method Based on TCN, BiGRU and Attention Mechanism

Abstract

With the increasing complexity of the network environment, the types of network attacks are gradually increasing. Network intrusion detection systems can detect and identify network attacks effectively. However, the existing methods have some limitations, focusing only on local or global temporal features of network traffic. To address the above issues, we present a novel network intrusion detection model (TGA) based on Temporal Convolutional Network (TCN), Bidirectional Gated Recurrent Unit (BiGRU), and self-attention mechanism. TCN extracts local temporal information from network traffic sequences, while BiGRU extracts global temporal information from network traffic sequences. However, TCN and BiGRU do not consider the weights of features when extracting them, so an attention mechanism is added. The feature vectors obtained in TCN and BiGRU are fused and then input into the self-attention mechanism to capture the correlation between different positions in the sequence and reassign the weights of the temporal features to further enhance the model’s capabilities. Lastly, it is delivered to the classifier to classify different network traffic classes. Our method achieves 97.83% accuracy on the public CSE-CIC-IDS2018 dataset. After extensive experiments, our idea proved to be reasonable and practical.