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

Abstract

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.

CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method

“…We also select five models in the existing researches to compare with the CBD model. The five models are one-dimensional CNN (1D-CNN) and two-dimensional CNN (2D-CNN) mentioned in [5], Stacked Autoencoder (SAE) mentioned in [2], a combination of CNN and LSTM (CNN-LSTM) mentioned in [4], and CLD-Net mentioned in [7]. The five models are all performed on the dataset mentioned in this paper.…”

“…In 2020, Pacheco et al [5] proposed a framework based on machine learning and deep learning for the classification of heterogeneous Internet traffic in satellite communications. The machine learning method chose rf, and the deep learning methods chose 1d and 2dcnn.…”

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

“…In addition to the above-mentioned topics, ML is applied to other relevant problems such as channel modeling [256]- [258], remote sensing [259]- [264], traffic prediction and classification [212], [265]- [267], and anti jamming [268]- [271].…”

Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance

“…Current ML methods are centered on network intrusion detection (NID) for cyberattacks, small-scale or special network setups, and anomaly detection using telemetry data on satellite and network devices [2]. The heterogeneity and complexity of connectivity options, architectures, and anomalous network events, including the high-impact low-frequency (HILF) incidents, will further make the ML-based solutions challenging on SICNs.…”