Construction of Internet Traffic Monitoring Model Based on Improved Transformer Algorithm

Abstract

With the popularization of the Internet, it is very important to effectively identify abnormal behaviors in network traffic. This study focuses on the construction of an internet traffic monitoring model, aiming to improve the accurate recognition rate of abnormal behavior and reduce information loss during small block segmentation. To this end, a internet traffic monitoring algorithm based on the improved Transformer is optimized. This model adopts a block segmentation algorithm that preserves important information during the segmentation process, thereby enhancing the segmentation quality and accuracy of the model. By effectively interacting with multiple receptive field information, the model reduces information loss and improves accuracy and efficiency. After experimental verification, the model performed well on CICIDS sample data, with an F1 value of 93% for normal internet traffic. The F1 value of internet attack traffic was 91%. Compared with the original Transformer model, it increased by 5% and 2.4%, respectively. On the NSLKDD sample, the improved algorithm proposed in the study had an area under the curve value of 0.90, which outperformed other models. This proves that it has significant advantages in the dual classification task of internet traffic anomaly monitoring. This study provides an effective deep learning algorithm for internet traffic anomaly monitoring, which is expected to provide strong support for network security assurance in practical application scenarios.

Keywords

• Deep learning
• transformer
• internet
• flow rate
• block segmentation
• monitoring