As network slicing is an enabling technology for the fifth-generation (5G) networks, it comes with complex challenges to ensure that resource management is consistent with slice tenant activities to provide better performance and cost-effective services to different tenants tailored to their needs. To this end, traffic classification is fundamental for the provisioning of the resources in a network by analyzing the network traffic to anticipate future requests. However, the massive increase of heterogeneous traffic features challenges dynamic network slices traffic classification. Previous literature have explored statistical and machine learning techniques but are constrained by feature engineering and computational costs. In this letter, we propose the multi-lane CapsNet assisted network traffic classification (MANTA), a framework based on multi-lane Capsule Networks (CapsNet) deep learning technique, to identify and classify heterogeneous traffic flows in 5G network slicing. Furthermore, we conduct a comparative analysis of the model with previous literature using deep learning techniques. The experimental results exhibit improved performance with high accuracy of 97.3975%, compared with other classifiers from previous literature.

中文翻译：

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MANTA：用于 5G 网络切片的多通道胶囊网络辅助流量分类


由于网络切片是第五代（5G）网络的一项使能技术，因此它面临着复杂的挑战，以确保资源管理与切片租户活动一致，从而为不同租户根据其需求提供更好的性能和更具成本效益的服务。为此，流量分类是通过分析网络流量来预测未来请求来配置网络资源的基础。然而，异构流量特征的大量增加对动态网络切片流量分类提出了挑战。先前的文献已经探索了统计和机器学习技术，但受到特征工程和计算成本的限制。在这封信中，我们提出了多通道 CapsNet 辅助网络流量分类（MANTA），这是一个基于多通道胶囊网络（CapsNet）深度学习技术的框架，用于识别和分类 5G 网络切片中的异构流量。此外，我们使用深度学习技术对该模型与之前的文献进行了比较分析。与之前文献中的其他分类器相比，实验结果显示出性能的提高，准确率高达 97.3975%。