Asliddin Sayidqulov

The 99 Percent Accuracy Illusion: A Base-Rate-Aware and Cross-Dataset Re-Evaluation of Machine-Learning Intrusion Detection

Machine-learning (ML) intrusion-detection systems are routinely reported to achieve 98-99% accuracy on standard benchmarks, widely read as evidence that the problem is solved. We challenge that reading through a controlled, leakage-safe, reproducible re-evaluation of four representative classifiers logistic regression,...