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.

翻译：随着现代计算机网络的复杂性和规模不断增长，迫切需要精确的流量分析，这在尖端无线连接技术中起着关键作用。本研究聚焦于利用机器学习方法构建先进的网络流量分类系统。我们提出了一种新颖的数据驱动方法，通过分析网络流量中的模式，能够实时高效地识别各种网络服务类型。该方法根据延迟需求将相似类型的网络流量组织成不同的类别，称为网络服务。此外，它将网络流量流分解为多个较小的流量流，每个流唯一携带特定服务。我们的机器学习模型在包含不同Wi-Fi网络条件下收集的各种网络服务类型标注样本的数据集上进行训练。评估结果表明，我们的系统在区分网络服务方面表现出卓越的准确性。这些结果强调了将人工智能融入无线技术的巨大潜力。这种方法有助于提高能源利用效率、增强服务质量保障并优化网络资源分配，从而为先进智能网络的发展奠定坚实基础。