## 3 Methodology
### Introducing Continuous Wavelet Transform (CWT) with Ricker Wavelet  
As mentioned in the previous sections, low-latency traffic shares some statistical properties with Gaussian noise, which provides a strong foundation for applying wavelet transform techniques. In this context, it becomes important to understand the complex nature of data flows within networks. Figure 2a shows the patterns of low-latency Internet traffic throughput of video conferencing and (b) Gaussian noise. The similarity observed in these patterns offers valuable insights, suggesting that low-latency traffic may exhibit noise-like characteristics. In signal processing, noise is conventionally perceived as unpredictable fluctuations or disturbances that can obscure crucial signal information [43]. In contrast, low-latency traffic exhibits a form of organized 'noise', a controlled and structured flow of data characterized by the frequent exchanges of small packets [4]. To further support this observation, Figure 3 presents the Probability Distribution Functions (PDFs) of both the low-latency traffic (b) and Gaussian noise (a). PDF analysis demonstrates that, when normalized, both signals share similar statistical properties, such as a classic bell-shaped curve, maximum density, and tails of the distribution. These similarities reinforce the idea that low-latency traffic can be modeled using the signal-processing techniques traditionally applied to noise in signal systems, such as wavelet transforms. This parallel draws attention to the fact that, while the nature of this data exchange might appear as noise to a casual observer, it is actually a deliberate and integral component within the network environment [44]. Figure 2: Parallel patterns: (**a**) low-latency traffic and (**b**) Gaussian noise. Obtaining representations in the frequency domain is crucial for several reasons. The frequency domain provides a different perspective on the data, highlighting patterns and characteristics that may not be apparent in the time domain. This is particularly valuable for analyzing non-stationary signals, where the signal properties change over time. Thus, all the extracted time-related features are processed using the continuous wavelet transform (CWT). The CWT is a powerful mathematical technique employed to analyze non-stationary signals in both the time and frequency domains. Unlike other transforms such as the Fourier Transform, the CWT decomposes a signal into various frequency components over time, enabling the capture of localized features and time-varying characteristics. This ability to capture fine-scale variations and changes in a signal makes the CWT particularly well suited for analyzing dynamic and irregular signals. Low-latency Internet traffic exhibits time properties similar to these signals, making CWT an effective tool for its classification. To better capture both short-term and long-term variations in Internet traffic, we apply wavelet transforms to key temporal features. This transformation provides a frequency-domain representation, exposing structured patterns in network traffic that traditional time-domain features fail to reveal. Figure 4 illustrates how core temporal features (e.g., throughput, slope, moving averages, and ratio) are mapped into their corresponding wavelet representations, which serve as enhanced inputs for the ANN model. Figure 3: Parallel distributions: (**a**) Gaussian noise and (**b**) low-latency traffic. The Ricker wavelet, shown in Equation (1), also known as the Mexican hat wavelet due to its resemblance to the cross-sectional profile of a Mexican hat, is a commonly used wavelet in time-frequency analysis. Unlike wavelets optimized for smooth or periodic signals (e.g., Daubechies and Morlet), the Ricker wavelet's sharp localization in both time and frequency domains enables the precise identification of transient patterns in low-latency traffic, akin to structured noise. This aligns with our observation that low-latency traffic exhibits noise-like statistical properties.