## 2 Related Work
Bio-inspired methods, such as artificial immune system algorithms, have also shown promise in optimizing classification models [35], while multi-stage classifiers have been proposed for handling encrypted and obfuscated traffic types [36; 37; 38]. While these approaches offer unique advantages, they still fall short in achieving the high-precision classification of low-latency traffic, particularly in mixed-traffic scenarios. In recent years, deep learning-based traffic classification has gained prominence, with CNN-based models such as Deep Packet [39] and LSTM-based solutions like FlowPic [40] achieving state-of-the-art results. These models leverage large-scale datasets, including those introduced by [PERSON] et al. [41] and [PERSON] et al. [42], which contain a mix of VPN, Tor, and unencrypted traffic. However, these solutions primarily focus on categorizing general Internet traffic and are not specifically optimized for low-latency classification. Furthermore, their reliance on end-to-end deep learning architectures often sacrifices interpretability, making them less suitable for real-time network management applications. A detailed analysis of the key approaches in Table 1 reveals distinct strengths and limitations. ANN-based methods [20; 23; 24] achieve high accuracy (97-99.08%) for encrypted traffic but rely exclusively on time-domain features, limiting their ability to capture high-frequency patterns in low-latency applications. The multilayer perceptron in [20] demonstrates scalability but requires full flow captures. DPI techniques [21] offer computational efficiency through Aho-Corasick pattern matching but fundamentally require payload inspection, rendering them ineffective for encrypted traffic and privacy-sensitive environments. Hybrid ML methods [27] incorporate wavelet kernels (GA-WK-ELM) for improved feature extraction but depend on evolutionary optimization that introduces significant computational overhead, hindering real-time deployment. CNN architectures [39; 40] attain state-of-the-art accuracy (up to 99.7%) through spatial feature learning but operate as black-box models with limited interpretability. FlowPic's [40] image-based representation enables efficient classification but discards temporal sequencing crucial for latency-sensitive traffic. As summarized in Table 1, recent studies achieved high accuracy (97-99.7%) in classifying encrypted/VPN traffic using ANNs, CNNs, and time-domain features (e.g., [20; 40]). Despite promising results in prior studies, existing traffic classification methods generally fall short in one or more of the following ways:  
* They rely on full payload data, which is inaccessible in encrypted traffic;
* They require complete flow captures, making them unsuitable for real-time use;
* They do not specifically target low-latency traffic, which has distinct statistical and temporal patterns. Also, datasets like ISCX and Darknet2020 (column \"Dataset\") support general application layer classification, and they lack benchmarks for dynamic, mixed low-latency scenarios. Existing methods struggle to differentiate low-latency traffic due to its bursty nature and overdependence on time-domain features. Although wavelet transforms excel in anomaly detection and QoS analysis, their integration with deep learning for traffic classification remains underexplored. Furthermore, ANN-based approaches (column \"Algorithms\") lack hybrid mechanisms to unify temporal and spectral representations. This paper addresses these gaps by proposing a novel hybrid ANN-Wavelet framework that synergizes time-domain statistical features with wavelet-decomposed frequency components, enhancing low-latency classification. The following sections detail our methodology, experiments, and results validating this approach.