Quality In / Quality Out: Assessing Data quality in an Anomaly Detection Benchmark

It is shown that relatively minor modifications on the same benchmark dataset (UGR’16, a UGR’16, a real-traffic dataset for anomaly detection) cause significantly more impact on model performance than the specific ML technique considered.