A Wearable Machine Learning Solution for Internet Traffic Classification in Satellite Communications

Pacheco, Fannia; Expósito, Ernesto; Gineste, Mathieu

doi:10.1007/978-3-030-33702-5_15

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…In this figure, an abstraction of a Satellite communication is displayed. This configuration is a simplification of an operational architecture already studied in one of our works [55]. This architecture takes the concerned network functions of a Satellite Architecture and the Policy-Based Network (PBN) architecture integrated with a classification system.…”

Section: Frameworkmentioning

confidence: 99%

“…These modifications depend on the architecture choices, and they will not affect the functional operations of our ML-based classification solution. The authors recently presented more details about the Satellite architecture, coupled with this framework in [55]. In that work, more formal implementation guidelines were established.…”

Section: Frameworkmentioning

confidence: 99%

“…We will evaluate different classical ML classifiers to perform this task with handcrafted features. In principle, based on our experience with this application [55], we found out that the most accurate ML approach is the tree-based one. Particularity, Random Forest (RF) highlights thanks to its capabilities for dealing with class imbalance; this ML model is also suitable for multi-label tasks.…”

Section: Discriminators and Classifiersmentioning

confidence: 99%

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

Pacheco

Expósito

Gineste³

2020

Computer Networks

Self Cite

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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.

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Section: Frameworkmentioning

confidence: 99%

Section: Frameworkmentioning

confidence: 99%

Section: Discriminators and Classifiersmentioning

confidence: 99%

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

Pacheco

Expósito

Gineste³

2020

Computer Networks

Self Cite

View full text Add to dashboard Cite

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“…ML is a tool that can be engaged effectively in enhancing network capacity and capabilities. In reference, 21 the accuracy of the ML‐based framework proposed for internet traffic classification may decrease as internet traffic applications evolve. However, for the application of DL in network traffic control systems, the time versus space trade‐off is a significant challenge, which can cause scalability issues in large systems like the Internet 8 …”

Section: Open Challengesmentioning

confidence: 99%

Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

Bhattacharyya

Nambiar

Ojha

et al. 2023

Satell Commun Network

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SummaryThe recent wave of creating an interconnected world through satellites has renewed interest in satellite communications. Private and government‐funded space agencies are making advancements in the creation of satellite constellations, and the introduction of 5G has brought a new focus to a fully connected world. Satellites are the proposed solutions for establishing high throughput and low latency links to remote, hard‐to‐reach areas. This has caused the injection of many satellites in Earth's orbit, which has caused many discrepancies. There is a need to establish highly adaptive and flexible satellite systems to overcome this. Machine Learning (ML) and Deep Learning (DL) have gained much popularity when it comes to communication systems. This review extensively provides insight into ML and DL's utilization in satellite communications. This review covers how satellite communication subsystems and other satellite system applications can be implemented through Artificial Intelligence (AI) and the ongoing open challenges and future directions.

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Graph Neural Networks for Traffic Classification in Satellite Communication Channels: A Comparative Analysis

Berezkin

et al. 2023

jour

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This paper presents a comprehensive comparison of graph neural networks, specifically Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT), for traffic classification in satellite communication channels. The performance of these GNN-based methods is benchmarked against traditional Multi-Layer Perceptron (MLP) algorithms. The results indicate that GNNs demonstrate superior accuracy and efficiency compared to MLPs, emphasizing their potential for application in satellite communication systems. Moreover, the study investigates the impact of various factors on GNN algorithm performance, providing insights into the most effective strategies for implementing GNNs in traffic classification tasks. This research offers valuable knowledge on the benefits and prospective use cases of GNNs within satellite communication systems.

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A Wearable Machine Learning Solution for Internet Traffic Classification in Satellite Communications

Cited by 3 publications

References 13 publications

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

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

Machine Learning and Deep Learning powered satellite communications: Enabling technologies, applications, open challenges, and future research directions

Graph Neural Networks for Traffic Classification in Satellite Communication Channels: A Comparative Analysis

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