Currently, the wide spreading of real-time applications such as VoIP and videos-based applications require more data rates and reduced latency to ensure better quality of service (QoS). A well-designed traffic classification mechanism plays a major role for good QoS provision and network security verification. Port-based approaches and deep packet inspections (DPI) techniques have been used to classify and analyze network traffic flows. However, none of these methods can cope with the rapid growth of network traffic due to the increasing number of Internet users and the growth of real time applications. As a result, these methods lead to network congestion, resulting in packet loss, delay and inadequate QoS delivery. Recently, a deep learning approach has been explored to address the time-consumption and impracticality gaps of the above methods and maintain existing and future traffics of real-time applications. The aim of this research is then to design a dynamic traffic classifier that can detect elephant flows to prevent network congestion. Thus, we are motivated to provide efficient bandwidth and fast transmision requirements to many Internet users using SDN capability and the potential of Deep Learning. Specifically, DNN, CNN, LSTM and Deep autoencoder are used to build elephant detection models that achieve an average accuracy of 99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the promising algorithms that does not require human class labeler. It achieves an accuracy of 97.95% with a loss of 0.13 . Since the loss value is closer to zero, the performance of the model is good. Therefore, the study has a great importance to Internet service providers, Internet subscribers, as well as for future researchers in this area.

翻译：摘要：当前，VoIP和视频类实时应用的广泛普及要求更高的数据传输速率和更低的延迟，以确保服务质量（QoS）。设计良好的流量分类机制对于保障QoS和网络安全验证具有关键作用。基于端口的方法和深度包检测（DPI）技术已被用于网络流量流的分类与分析。然而，随着互联网用户数量的持续增长及实时应用的快速发展，这些方法已无法应对网络流量的急剧增长。由此导致网络拥塞，引发数据包丢失、延迟增加以及QoS无法充分保障等问题。近年来，深度学习技术被探索用于解决上述方法耗时且实用性不足的问题，并满足实时应用的现有和未来流量需求。本研究旨在设计一种能够检测大象流以预防网络拥塞的动态流量分类器。因此，我们利用SDN能力与深度学习潜力，致力于为众多互联网用户提供高效带宽和快速传输要求。具体而言，我们采用深度神经网络（DNN）、卷积神经网络（CNN）、长短期记忆网络（LSTM）和深度自编码器构建了大象流检测模型，其平均准确率分别达到99.12%、98.17%和98.78%。深度自编码器作为一种无需人工标注类别标签的算法，准确率达97.95%，损失值为0.13。由于损失值趋近于零，模型性能表现良好。因此，本研究对互联网服务提供商、网络用户以及该领域的未来研究者均具有重要意义。