With the rapid development of network technology, the Internet has accelerated the generation of network traffic, which has made network security a top priority. In recent years, due to the limitations of deep packet inspection technology and port number-based network traffic identification technology, machine learning-based network traffic identification technology has gradually become the most concerned method in the field of traffic identification with its advantages. As the learning ability of deep learning in machine learning becomes more substantial and more able to adapt to highly complex tasks, deep learning has become more widely used in natural language processing, image identification, and computer vision. Therefore, more and more researchers are applying deep learning to network traffic identification and classification. To address the imbalance of current network traffic, we propose a traffic identification model based on generating adversarial deep convolutional networks (GADCN), which effectively fits and expands traffic images, maintains a balance between classes of the dataset, and enhances the dataset stability. We use the USTC-TFC2016 dataset as training and test samples, and experimental results show that the method based on GADCN has better performance than general deep learning models.

随着网络技术的飞速发展，互联网加速了网络流量的产生，这使得网络安全成为重中之重。近年来，由于深度包检测技术和基于端口号的网络流量识别技术的局限性，基于机器学习的网络流量识别技术以其优势逐渐成为流量识别领域最受关注的方法。随着深度学习在机器学习中的学习能力越来越强大，对高度复杂的任务的适应能力越来越强，深度学习在自然语言处理、图像识别、计算机视觉等领域的应用越来越广泛。因此，越来越多的研究人员将深度学习应用于网络流量识别和分类。为了解决当前网络流量的不平衡问题，我们提出了一种基于生成对抗性深度卷积网络（GADCN）的流量识别模型，该模型有效地拟合和扩展了流量图像，保持了数据集类别之间的平衡，增强了数据集的稳定性。我们使用USTC-TFC2016数据集作为训练和测试样本，实验结果表明基于GADCN的方法比一般的深度学习模型具有更好的性能。