As the complexity and scale of modern computer networks continue to increase,
there has emerged an urgent need for precise traffic analysis, which plays a
pivotal role in cutting-edge wireless connectivity technologies. This study
focuses on leveraging Machine Learning methodologies to create an advanced
network traffic classification system. We introduce a novel data-driven
approach that excels in identifying various network service types in real-time,
by analyzing patterns within the network traffic. Our method organizes similar
kinds of network traffic into distinct categories, referred to as network
services, based on latency requirement. Furthermore, it decomposes the network
traffic stream into multiple, smaller traffic flows, with each flow uniquely
carrying a specific service. Our ML models are trained on a dataset comprised
of labeled examples representing different network service types collected on
various Wi-Fi network conditions. Upon evaluation, our system demonstrates a
remarkable accuracy in distinguishing the network services. These results
emphasize the substantial promise of integrating Artificial Intelligence in
wireless technologies. Such an approach encourages more efficient energy
consumption, enhances Quality of Service assurance, and optimizes the
allocation of network resources, thus laying a solid groundwork for the
development of advanced intelligent networks.

利用机器学习方法，该研究介绍了一种高级网络流量分类系统，能够实时分析网络流量并识别各种网络服务类型。通过对网络流量中的模式进行分析，我们的方法将相似的网络流量归类为不同的网络服务，并将流量分解为多个小的流，每个流专门用于承载特定的服务。我们的机器学习模型基于包含不同网络服务类型的标记示例的数据集进行训练，并在评估中展现了出色的准确性。这些结果强调了将人工智能集成到无线技术中的巨大潜力，通过这种方法可以实现更高效的能源消耗、提供更好的服务质量保证以及优化网络资源的分配，为先进智能网络的发展打下坚实的基础。