  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.
