基于视觉感知特征的手机应用流量识别方法

由于大多数手机应用通过HTTP协议进行通信，传统的端口识别方法已经基本失效。另外，深度包检测和基于流统计特征的机器学习方法均存在手工设计特征和标记样本的困难。借鉴计算机视觉领域的优势，提出了一种基于视觉感知特征的手机应用流量识别方法。首先，将应用层载荷数据转换为视觉上有意义的图像，并从网络关口采集真实数据，建立了样本数据集IMTD17；然后，设计了具有视觉特征提取能力的卷积感知网络模型2D-CPN，利用卷积自编码实现了对大量无标记样本的学习，并通过多类型回归建立起从隐层特征到应用类型的映射。实验结果表明，该方法的流量识别准确率满足实际使用的需求。

Mobile app traffic identification based on visual perception features

The mobile apps mostly communicate with servers via HTTP, which makes port-based method ineffective. Furthermore, depth packet inspection and flow-based classifiers have difficulties in designing features and labeling samples manually. Motivated by the excellence of computer vision, this paper proposed a method of mobile app traffic identification based on visual perception features. First, it converted the app traffic flows into vision-meaningful images. Collecting real traffic from the network gateway, it created the IMTD17 dataset. Then, it designed a two-dimensional convolutional perception network (2D-CPN) with the ability of visual feature extraction. The network realized the learning of massive unlabeled samples by the convolutional autoencoder, and used multi-class regression to create the mapping from the latent feature to the app categories. The experimental results show that the identification accuracy of the approach satisfies the practical requirement.