## 7 Conclusions and Future Works  
In this study, we have presented a novel approach for the classification and identification of low-latency Internet traffic using deep learning techniques and trend-based features. By incorporating advanced trend features such as slope, moving averages, download-to-upload ratio, and wavelet transform, we have demonstrated the effectiveness of our methodology in accurately classifying different types of Internet traffic. Experimental results have shown that our model achieved high accuracy rates of classifying FTP, video streaming, and low-latency traffic with 99.09%, 99.3%, and 99.56%, respectively. These results validate the robustness and efficacy of our approach in accurately classifying primary Internet traffic types. Furthermore, our experiments with mixed traffic scenarios, both basic and complex, have provided valuable insights into the performance of our model in real-world traffic mix situations. We observed that as the complexity and number of traffic types increased, the accuracy of identifying the existence of low-latency in the traffic mix decreased. However, our model still showed promising performance in detecting low-latency traffic within mixed traffic scenarios. In general, the integration of wavelet transform and deep learning techniques has proven to be instrumental in enhancing the accuracy and precision of Internet traffic classification, particularly in the context of identifying low-latency traffic. Our findings contribute to the advancement of traffic analysis methodologies and have practical implications for optimizing the delivery of time-sensitive applications over the Internet. In conclusion, our work underscores the significance of accurately identifying and prioritizing low-latency Internet traffic, and our proposed methodology offers a promising solution for addressing this critical aspect of network traffic management. Future research directions could focus on enhancing the scalability and adaptability of the proposed framework. Validating the model on large-scale, publicly available datasets encompassing diverse network environments--such as 5G, IoT, and edge computing scenarios--would strengthen its generalizability. Exploring advanced deep learning architectures, such as attention-based transformers or hybrid CNN-LSTM models, could further improve classification accuracy in highly dynamic or encrypted traffic scenarios. Additionally, optimizing wavelet transform computations through hardware acceleration or lightweight algorithms would reduce computational overhead, enabling deployment on resource-constrained edge devices. Integrating online learning mechanisms to dynamically adapt to evolving traffic patterns and emerging applications would address real-time network variability. Extending the framework to classify encrypted traffic and support multi-class scenarios with finer-grained application distinctions (e.g., differentiating between gaming, VoIP, and financial trading) could broaden its applicability. Finally, investigating federated learning approaches for privacy-preserving traffic classification in distributed networks represents another promising avenue. These enhancements would solidify the framework's utility in next-generation network management systems. Conceptualization, R.E. and V.R. ; methodology, R.E. and V.R. ; software, R.E. ; validation, R.E. and V.R. ; formal analysis, R.E. and V.R. ; investigation, R.E. and V.R. ; resources, R.E. ; data curation, R.E. and V.R. ; writing--original draft preparation, R.E. and V.R. ; writing--review and editing, R.E. and V.R. ; visualization, R.E. and V.R. ; supervision, V.R. ; project administration, R.E. and V.R. ; funding acquisition, V.R. All authors have read and agreed to the published version of the manuscript. This work was supported by the Ministry of Education of Turkey through a doctoral scholarship awarded to [PERSON]. No data is being made available for the work presented in the paper. [PERSON] is a PhD candidate at City St George's, University of London.