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.

如今，Internet网络系统已成为通信，交易和娱乐等平台。该通信系统的特点是地面和卫星组件之间相互交互，以提供端点之间信息的传输路径。特别是，卫星通信提供商的兴趣是通过最佳利用按需可用资源并提供服务质量（QoS）来提高客户满意度。改善QoS意味着减少与信息丢失和卫星通信中Internet数据包延迟相关的错误。在这种意义上，根据Internet流量（流，VoIP，浏览等）和那些错误情况，可以将Internet流分为不同的敏感和非敏感类别。遵循这个想法，这项工作旨在寻找新的Internet流量分类方法来改善QoS。将研究并部署机器学习（ML）和深度学习（DL）技术以对Internet流量进行分类。将评估在众所周知的卫星通信和QoS管理架构上耦合ML或DL解决方案的所有必要元素。要开发此解决方案，需要丰富而完整的Internet流量集。在这种情况下，模拟的卫星通信平台将用作数据生成环境，在其中将启动和捕获不同的Internet通信。建议的分类系统将处理不同的Internet通信（加密，未加密和隧道传输）。该系统将分层处理传入的流量，以实现较高的分类性能。