<ul><li>Machine learning has been used for network traffic classification for over two decades.</li><li>Recent findings suggest that a simple k-NN baseline using packet sequences metadata can perform as well as or even better than complex neural networks.</li><li>Analysis reveals that many datasets contain over 50% redundant samples, impacting model performance and accuracy estimation.</li><li>The study suggests that standard machine learning practices may not be suitable for network traffic classification and proposes new directions for evaluation in the field.</li></ul>

When Simple Model Just Works: Is Network Traffic Classification in Crisis?

Discover more