将网络包数据转为图像，用CNN提升分类精度与隐私保护。
Improving the network traffic classification using the Packet Vision approach
- 将包头和载荷数据转化为图像输入CNN
- 在4类流量上达到优异分类性能
- 兼顾隐私安全，适合智能网络架构
网络流量分类有助于优化网络管理与服务提供。未来移动网络等架构需具备应用感知能力。卷积神经网络（CNN）在多个领域表现优异，可应用于流量分类。为此，我们提出Packet Vision方法，将原始包数据（含头部与载荷）转换为图像，适配CNN输入。该方法在保障安全与隐私的同时，显著优于现有技术。我们构建了包含四类流量的数据集，评估了AlexNet、ResNet-18与SqueezeNet三种CNN架构。实验表明，Packet Vision结合CNN具有高适用性与卓越分类性能，是实现智能网络流量识别的有力方案。
原文摘要 · Abstract (English)
The network traffic classification allows improving the management, and the network services offer taking into account the kind of application. The future network architectures, mainly mobile networks, foresee intelligent mechanisms in their architectural frameworks to deliver application-aware network requirements. The potential of convolutional neural networks capabilities, widely exploited in several contexts, can be used in network traffic classification. Thus, it is necessary to develop methods based on the content of packets transforming it into a suitable input for CNN technologies. Hence, we implemented and evaluated the Packet Vision, a method capable of building images from packets raw-data, considering both header and payload. Our approach excels those found in state-of-the-art by delivering security and privacy by transforming the raw-data packet into images. Therefore, we built a dataset with four traffic classes evaluating the performance of three CNNs architectures: AlexNet, ResNet-18, and SqueezeNet. Experiments showcase the Packet Vision combined with CNNs applicability and suitability as a promising approach to deliver outstanding performance in classifying network traffic.
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