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首页> 外文期刊>電子情報通信学会技術研究報告. 技術と社会·倫理. Social Implications of Technology and Information Ethics >Towards Self-Optimizing Network: Applying Deep Learning to Network Traffic Categorization and Identification in the Context of Application-Aware Network
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Towards Self-Optimizing Network: Applying Deep Learning to Network Traffic Categorization and Identification in the Context of Application-Aware Network

机译：朝来自我优化网络：在应用程序感知网络上下文中应用深度学习与网络流量分类和标识

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摘要

The application-aware routing is a network routing technology optimized for a network with an inconsistent link performance, a problem which is common for a multi-institution research and academic network. Using the application-aware routing, an application-aware network routes each flow independently via the optimal path corresponding to the identified application characteristic. This technology enables the creation of a self-optimizing network. However, an automatic network flow categorization and identification system is required. In the scope of this work, network flow categorization is defined as the process of generating a meaningful classification whereas network flow identification is defined as identifying which class a network flow belongs to. These are challenging problems with various applicabilities. We present a deep learning approach to network flow categorization and identification problems. Deep learning provides several advantages over existing solutions in the context of the application-aware network. According to our experiments, a 3-layer stacked denoising autoencoder trained with CAIDA Internet traffic dataset produces the most meaningful classification and a useful class identifier (classifier). This deep neural network (DNN) model generates three-classes classification: a bandwidth-bound pattern, a latency-bound pattern, and an irregular pattern. A design of a highly scalable implementation of a self-optimizing network using a DNN model is also presented with justification for each design decision. Our findings suggest that a deep learning approach to network flow categorization and identification problems in the context of the application-aware network and the self-optimizing network are promising.
机译：应用程序感知路由是一种网络路由技术，针对具有不一致的链路性能的网络进行了优化，是多机构研究和学术网络的常见问题。使用应用程序感知路由，通过与所识别的应用特性对应的最佳路径独立地，可以独立地传输应用程序感知的路由。该技术使得能够创建自我优化网络。但是，需要自动网络流量分类和识别系统。在这项工作的范围内，网络流分类被定义为生成有意义的分类的过程，而网络流识别被定义为识别网络流所属的哪个类。这些都是挑战各种应用的问题。我们提出了一种深入的学习方法来网络流量分类和识别问题。在应用程序感知网络的上下文中，深度学习提供了对现有解决方案的几个优点。根据我们的实验，用CAIDA Internet流量数据集培训的3层堆叠的去噪AutoEncoder产生了最有意义的分类和有用的类标识符（分类器）。该深度神经网络（DNN）模型生成三类分类：带宽模式，延迟绑定模式和不规则图案。使用DNN模型的自我优化网络的高度可扩展性实现的设计也具有对每个设计决策的理由。我们的研究结果表明，在应用程序感知网络和自我优化网络的背景下，对网络流分类和识别问题的深度学习方法是有前途的。

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