MVTC: Data and Knowledge-Based Distributed Multiview Information Mixing Network for Traffic Classification in Internet of Unmanned Agents

Yang Liu, Zhenkun Fu, Yufeng Zhan, Yuanqing Xia

Abstract: In the industrial IoT scenario, where massive data generation occurs, network traffic classification is crucial for operational security. The Internet of Unmanned Agents (IUA) is an emerging concept within the IoT framework. It focuses on the connectivity and interaction of various unmanned agents, such as drones, autonomous robots, and smart sensors. These unmanned agents collect and transmit large amounts of data in real-time, further contributing to the complexity of data in the IoT environment. The IUA aims to enable seamless cooperation and coordination among these agents, enhancing the overall efficiency and intelligence of industrial operations. The primary challenges in the IUA scenario lie in developing effective models and meeting real-time processing demands. Traditional methods struggle with large, high-dimensional data, while transformer-based models, although achieving good results, are difficult to deploy due to their size, training times, and complex tuning. In this article, we introduce a simple distributed architecture MVTC, which incorporates prior domain knowledge and delivers comparable results to transformer-based models but with shorter processing times and easier deployment. And it does not require large-scale unlabeled data for pretraining, which makes it highly suitable for real-world network traffic classification. The experiments demonstrate that the proposed method outperforms most existing approaches by up to 1.53% while using only 15.26% of the parameters.

External IDs:dblp:journals/iotj/LiuFZX25