[KongchangAI]
Benchmark

UNSW-NB15

A widely used network intrusion detection dataset containing multiple attack types. It is commonly used to evaluate the detection and classification performance of traffic classification models.

Timeline (last 90 days)

Oct 22

On the three benchmarks UNSW-NB15, CIC-IDS2017, and EMBER2024, the paper compares against calibrated XGBoost, Random-Forest, MLP, and Kitsune, and measures a two-stage cascade of "Suricata (52,674 Emerging-Threats rules) → VX-One" running over five days on CIC-IDS2017.

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Oct 22

VX-One wins on the detection metrics (AUROC, FPR@95%TPR) on UNSW-NB15, and also leads under the temporal drift conditions of CIC-IDS2017 (0.711 vs 0.637).

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Oct 22

In the leave-one-family-out test, the VX-One gate successfully detected never-before-seen attack families on all 9 folds of UNSW (9/9), but on the CIC recon family its detection rate fell below random level.

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Oct 22

Leave-one-family-out cross-validation (LOFO) is a protocol for evaluating generalization to unknown attacks: each time one attack family is completely removed, the model is trained on the remaining families and then tested on the removed family; UNSW-NB15 has 9 attack families, hence 9 folds.

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