A Novel Traffic Classifier With Attention Mechanism for Industrial Internet of Things
Ruijie Zhao, Yiteng Huang, Xianwen Deng, Yongyong Shi, Jiabin Li, Zijing Huang, Yijun Wang, Zhi Xue
Abstract
With the development of the Industrial Internet of Things (IIoT), the complex traffic generated by large-scale IIoT devices presents challenges for traffic analysis. Most of existing deep learning-based traffic analysis methods use a single flow for classification, resulting in being misled by the irrelevant flow. Thus, it is necessary to use flow sequences for traffic analysis. However, existing models fail to effectively distinguish unimportant flows in flow sequence, which affects the classification performance. To address the aforementioned challenges, we propose a novel traffic classifier called flow transformer to perform traffic analysis with flow sequences, which leverages multihead attention mechanism to strengthen the information interaction between related flows. Besides, the RF-based feature selection method is designed to select the optimal feature combination, avoiding insignificant features from reducing the performance of the classifier. Experimental results on three real-world traffic datasets demonstrate that our method outperforms state-of-the-art methods with a large margin.