Abstract:

In the era of rapid development of the Internet, the number of people interacting with each other on the Internet is increasing, making network security particularly important. This paper aimed to enhance the model's ability to detect abnormal traffic, and proposed a capsule network model based on the attention mechanism. In the feature extraction stage and the dynamic routing stage, the attention mechanism was incorporated to enhance the model's ability to extract key features and improve the accuracy of intrusion detection tasks. Through experiments on the NSL-KDD data set and the CICDS2017 data set, experimental results show that the model in this paper is higher than other models in terms of generalization ability, and the accuracy rate on the CICIDS2017 test set has reached 97.56%. The accuracy of the NSL-KDD test set can reach 95.88%, which is significantly more efficient than other traditional intrusion detection models.

Key words: deep learning, intrusion detection, capsule network, attention mechanism