Comparative predictive modelling of machine learning techniques for network traffic analysis
This research article evaluates five machine learning regression algorithms for predicting network traffic behavior using the UNSW-NB15 dataset. The study finds that XGBoost and LightGBM offer the highest predictive accuracy for managing modern network traffic.
Why it matters
Improved predictive modeling for network traffic is essential for optimizing digital infrastructure and managing the increasing complexity of global communication networks.
Scientific Reports ( 2026 ) Cite this article
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