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arXiv Machine Learning By Shenghe Xu, Lifan Mei

A First Glance at Jev for Network Traffic Classification: Accuracy, Processing Time, and Cost

Read the original on arXiv Machine Learning →

The paper evaluates Jev, a general-purpose decision model, for classifying network traffic using only the first ten packets’ sizes, directions, and inter‑packet times. Across 52,000 records, adding 40 labeled examples improves Jev’s accuracy from 9.80% to 28.42%, yet Random Forest and Extra Trees models trained on 8,000 records achieve higher accuracies of 69.95% and 66.80%. Even with 150 examples, Jev reaches only 34.50% accuracy, and in a paired 100‑record test, Jev’s 29% accuracy and 0.750 s median request time are lower than the generative OpenAI GPT‑5.6 Sol’s 37% accuracy and 6.036 s response time, though Jev incurs lower API charges.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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