## 2 Related Work  
The rapid growth of high-speed Internet services has driven a surge in demand for efficient traffic management and optimization strategies as highlighted in the Cisco report [11]. With increasing reliance on real-time applications such as online gaming, video conferencing, and financial trading, the ability to accurately classify low-latency Internet traffic has become a critical research challenge [12, 13, 14]. However, existing classification methods often struggle with the dynamic and bursty nature of low-latency traffic, necessitating novel approaches that move beyond traditional techniques. Early traffic classification methods relied on port-based heuristics, deep packet inspection (DPI), and statistical analysis [15, 16, 17, 18]. While these approaches provided initial insights into network traffic behavior, they suffer from severe limitations in modern networks. Port-based methods are increasingly unreliable due to dynamic port allocations and encryption techniques that obscure traffic signatures [19]. DPI, while highly accurate, raises significant privacy concerns and requires high computational overhead, making it impractical for large-scale deployments [20]. Statistical approaches, though lightweight, often fail when dealing with encrypted traffic, which now dominates Internet communications [21]. To address these shortcomings, machine learning (ML) and deep learning (DL) models have gained traction in traffic classification research. ANNs, in particular, have demonstrated remarkable success in recognizing complex traffic patterns [22; 23]. They have been applied to traffic classification, anomaly detection, and predictive modeling, offering adaptability to evolving network conditions [24; 25; 26]. However, a major challenge with ANN-based methods is their reliance on time-domain traffic features, which often fail to capture the high-frequency fluctuations characteristic of low-latency traffic. As a result, standard ANNs struggle to differentiate between low-latency and non-low-latency traffic in complex network environments. To enhance feature extraction and improve classification accuracy, researchers have turned to signal-processing techniques such as wavelet transform (WT). Wavelet analysis has been widely used in time-series data processing, particularly in network traffic prediction [27], anomaly detection [28], and QoS monitoring [29]. Unlike traditional statistical methods, wavelets decompose network traffic into multiple frequency scales, allowing for a more granular representation of the underlying patterns. This capability is particularly useful for low-latency traffic, which exhibits noise-like fluctuations that are difficult to detect in the time domain alone [30]. Beyond wavelet transformations, recent advancements in Internet traffic classification have focused on trend-based features that enhance model interpretability. Studies have introduced fine-grained statistical features such as moving averages, throughput patterns, and slope-based metrics, which have led to significant improvements in classification accuracy [4; 18; 31]. Additionally, feature selection techniques--such as multifractal analysis and PCA-based selection--have been applied to refine classification models, further improving performance and robustness [30; 32]. Despite these advances, most studies still rely solely on time-domain features, which limits their ability to fully exploit the high-frequency patterns unique to low-latency traffic. Parallel to ML-based solutions, researchers have also explored alternative traffic classification approaches to improve scalability and real-time adaptability. Incremental Support Vector Machines (SVMs) have been developed to handle dynamic traffic patterns, allowing for adaptive classification in real-world networks [33]. Other work has explored hardware-accelerated classification techniques, such as FPGA-based decision trees, which enable high-speed traffic analysis with minimal computational overhead [34].