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A framework to classify heterogeneous Internet traffic with Machine Learning and Deep Learning techniques for satellite communications

机译：将异构互联网流量与机器学习和卫星通信深层学习技术进行分类的框架

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摘要

Nowadays, the Internet network system serves as a platform for communication, transaction, and entertainment, among others. This communication system is characterized by terrestrial and Satellite components that interact between themselves to provide transmission paths of information between endpoints. Particularly, Satellite Communication providers' interest is to improve customer satisfaction by optimally exploiting on demand available resources and offering Quality of Service (QoS). Improving the QoS implies to reduce errors linked to information loss and delays of Internet packets in Satellite Communications. In this sense, according to Internet traffic (Streaming, VoIP, Browsing, etc.) and those error conditions, the Internet flows can be classified into different sensitive and non-sensitive classes. Following this idea, this work aims at finding new Internet traffic classification approaches to improving the QoS. Machine Learning (ML) and Deep Learning (DL) techniques will be studied and deployed to classify Internet traffic. All the necessary elements to couple an ML or DL solution over a well-known Satellite Communication and QoS management architecture will be evaluated. To develop this solution, a rich and complete set of Internet traffic is required. In this context, an emulated Satellite Communication platform will serve as a data generation environment in which different Internet communications will be launched and captured. The proposed classification system will deal with different Internet communications (encrypted, unencrypted, and tunneled). This system will process the incoming traffic hierarchically to achieve a high classification performance. Finally, some experiments on a cloud emulated platform validates our proposal and set guidelines for its deployment over a Satellite architecture.
机译：如今，互联网网络系统用作通信，交易和娱乐的平台等。该通信系统的特征在于陆地和卫星组件，其在自己之间交互，以提供端点之间的信息的传输路径。特别是，卫星通信提供商的兴趣是通过最佳利用可用资源和提供服务质量（QoS）来提高客户满意度。改进QoS意味着减少与卫星通信中的互联网数据包的信息丢失和延迟相关的错误。从这个意义上讲，根据互联网流量（流，VoIP，浏览等）和那些错误条件，Internet流可以分类为不同的敏感和非敏感类。在这个想法之后，这项工作旨在找到新的互联网流量分类方法来改善QoS。将研究和部署机器学习（ML）和深度学习（DL）技术以对互联网流量进行分类。将评估将ML或DL解决方案耦合众所周知的卫星通信和QoS管理架构的所有必要元素。要开发此解决方案，需要丰富和完整的互联网流量。在这种情况下，模拟卫星通信平台将用作数据生成环境，其中将启动和捕获不同的互联网通信。拟议的分类系统将处理不同的互联网通信（加密，未加密和隧道）。该系统将分层处理传入的流量以实现高分类性能。最后，在云模拟平台上的一些实验验证了我们的提案和设定了在卫星架构上部署的指导。

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