Network Slicing and Traffic Classification in 5G Networks with Explainable Machine Learning
Lecture Notes in Networks and Systems, ISSN: 2367-3389, Vol: 997 LNNS, Page: 641-658
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
To improve network management in 5G networks by utilizing network slicing and traffic classification, we propose explainable machine learning techniques. For achieving interpretability, we devised a novel, general-purpose filter-based feature selection method, namely, extended t-statistic suitable for multi-class classification problems. Real-world network traffic data is used to test the effectiveness of our approach. We found that our method yielded high accuracy accompanied by interpretability. This is significant because it can improve the overall performance of 5G networks by enhancing the accuracy and trustworthiness of network slices and traffic classification. After applying extended t-statistic, with top 20 features, XGBoost obtained highest F1-Score of 98.3%.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85200678160&origin=inward; http://dx.doi.org/10.1007/978-981-97-3242-5_42; https://link.springer.com/10.1007/978-981-97-3242-5_42; https://dx.doi.org/10.1007/978-981-97-3242-5_42; https://link.springer.com/chapter/10.1007/978-981-97-3242-5_42
Springer Science and Business Media LLC
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