## 1 Introduction  
The digital age has brought a tremendous demand for high-speed Internet services, fueling the search for efficient traffic management and optimization strategies. One critical branch of this effort is the accurate identification and classification of low-latency Internet traffic. Low-latency networking demands end-to-end delays below 100 ms for real-timeapplications (e.g., video conferencing and online gaming), and below 10 ms for mission-critical systems like autonomous vehicles or financial trading [1, 2, 3]. For instance, video conferencing suffers from \"freezing\" and audio-video de-synchronization with delays \(>\)150 ms, degrading user experience. Online gaming requires \(<\)50 ms latency to prevent gameplay disadvantage (e.g., delayed reactions in competitive matches). Financial trading platforms (e.g., algorithmic trading) incur revenue losses with delays \(>\)1 ms due to missed arbitrage opportunities. Autonomous systems (e.g., vehicle-to-vehicle communication) risk collisions if control signals exceed 10 ms thresholds. Accurate classification of such traffic is foundational for QoS-aware resource allocation, where millisecond-level delays directly impact safety, revenue, and user satisfaction.  
Traditional approaches to latency management rely on real-time monitoring [4], statistical analysis [5], and Quality-of-Service (QoS) metrics [6]. Streaming platforms like Netflix and YouTube, for instance, dynamically adjust video quality by monitoring bandwidth and allocating resources to minimize buffering [7]. However, these methods often lack the granularity to distinguish low-latency traffic's unique characteristics, which resemble Gaussian noise due to consistent bidirectional micro-packet exchanges. This \"noise-like\" pattern, when analyzed through signal processing techniques such as wavelet transforms, reveals high-frequency components indicative of rapid packet dynamics [8].  
Wavelet transforms (WTs) are particularly suited for decomposing traffic signals into time-frequency domains, enabling the isolation of transient features like throughput fluctuations, slope variations, and moving averages. These temporal metrics, when combined with bidirectional ratios, provide a multi-scale perspective essential for differentiating low-latency traffic from bulk data transfers. Building on this, our methodology integrates artificial neural networks (ANNs) with wavelet-transformed features to enhance classification accuracy. By exposing high-frequency patterns often obscured in raw time-series data, the model captures both temporal dynamics and spectral signatures inherent to low-latency flows.  
This hybrid approach addresses a critical challenge: distinguishing structured low-latency \"noise\" from background network \"signal\" with precision. Unlike conventional noise reduction techniques in signal processing [9, 10], our framework leverages ANNs to adaptively learn traffic patterns, ensuring robust classification even in dynamic network environments. The result is a system capable of near real-time prioritization, crucial for applications demanding instantaneous responsiveness.  
In the forthcoming sections of this article, we will explore our methodology in a comprehensive way, dive into the details of data collection, present our experimental findings, and engage in thoughtful discussions. The goal is to vividly demonstrate the effectiveness of our approach in classifying and identifying low-latency traffic.