An attention-based convolutional neural network for intrusion detection model.

Abstract

Network technology has had a distinctive impact on the entire human civilization and has become an important factor of production in many countries and regions. However, with the widespread popularity of network technology, security flaws have been scattered in various fields, and potential crises may break out by attackers at any time. Therefore, it is crucial to establish a traffic monitoring mechanism for network systems. Some researchers have already implemented intrusion detection models by convolutional neural networks (CNNs) combined with attention mechanisms and achieved good results. However, few attempts have been made to improve the computational efficiency of the model by organizing the appropriate image structure, and the integration of attention mechanisms could be further enhanced. In this study, an attention-based CNN intrusion detection model has been proposed. Together with the image generation methods described in this paper, an efficient and accurate processing flow is formed. To optimize the use of the feature information in the experiments, the feature fields in the experimental images were arranged according to the results of their importance analysis. And a more integrated attention mechanism has been applied to the CNN for building the detection model. A series of comparative experiments were conducted on a subset of the CSE-CIC-IDS2018 dataset, and the results show that the detection process and model proposed in this paper can swiftly complete the detection procedure while maintaining high accuracy.