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：当前，诸如VoIP和视频类应用等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，以确保更好的服务质量（QoS）。一个设计良好的流量分类机制对于实现优质QoS保障和网络安全验证至关重要。基于端口的检测方法和深度包检测（DPI）技术已被用于网络流量流的分类与分析。然而，由于互联网用户数量的持续增长以及实时应用的激增，这些方法均难以应对网络流量的快速增长。因此，这些方法会导致网络拥塞，进而引发数据包丢失、延迟以及QoS保障不足等问题。近期，深度学习方法被探索用于解决上述方法耗时且实用性不足的缺陷，并维护现有及未来实时应用的流量。本研究的目的是设计一种能够检测大象流以防止网络拥塞的动态流量分类器。为此，我们利用SDN的能力和深度学习的潜力，为众多互联网用户提供高效带宽和快速传输需求。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，分别达到了99.12%、98.17%和98.78%的平均准确率。此外，深度自编码器是一种无需人工类别标注的极具前景的算法，其准确率达到97.95%，损失值为0.13。由于损失值接近零，表明该模型性能良好。因此，本研究对互联网服务提供商、互联网用户以及该领域的未来研究者具有重要意义。