Multi-Class, Multi-Tier Network Intrusion Detection: A Comprehensive and Reproducible Benchmark
Machine learning (ML) and deep learning (DL) have dominated Intrusion Detection System (IDS) research in recent years. Unfortunately, many existing studies have produced inflated results and unreliable benchmarks due to critical oversights and mistakes in the ML and DL pipeline, from data collection and labeling to feature engineering and model training and evaluation. CIC-IDS2017 is a standard benchmark for network intrusion detection. Still, many published results on this dataset are difficult to compare due to labeling errors, inconsistent flow extraction, potential leakage, and performance evaluation metrics dominated by benign traffic. In this paper, we present a comprehensive benchm...
| Authors | Yufeng Xin, Bryant Goseland, Mohamed Rahouti |
|---|---|
| Primary category | cs.CR |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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