ArticleSensors (Basel, Switzerland)2021
A Comparative Study of Traffic Classification Techniques for Smart City Networks.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The impact of the COVID-19 pandemic on airlines' passenger satisfaction.Journal of air transport management · 2023Article
- An Evaluation Process for IoT Platforms in Time-Sensitive Domains.Sensors (Basel, Switzerland) · 2022Article
- Evaluation of Heat-Induced Damage in Concrete Using Machine Learning of Ultrasonic Pulse Waves.Materials (Basel, Switzerland) · 2022Article
- Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Smart city networks involve many applications that impose specific Quality of Service (QoS) requirements, thus representing a challenging scenario for network management. Solutions aiming to guarantee QoS support have not been deployed in large-scale networks. Traffic classification is a mechanism used to manage different aspects, including QoS requirements. However, conventional traffic classification methods, such as the port-based method, are inefficient because of their inability to handle dynamic port allocation and encryption. Traffic classification using machine learning has gained research interest as an alternative method to achieve high performance. In fact, machine learning embeds intelligence into network functions, thus improving network management. In this study, we apply machine learning algorithms to predict network traffic classification. We apply four supervised learning algorithms: support vector machine, random forest,
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.