PlumX Metrics
Embed PlumX Metrics

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
  • 0
    Citations
  • 0
    Usage
  • 0
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

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%.

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know