LATIDIA · Ciberseguridad

Detección de intrusiones de red multiclase y de varios niveles: un punto de referencia integral y reproducible

arXiv: 2609.36039v1Tipo de anuncio: nuevo Resumen: El aprendizaje automático (ML) y el aprendizaje profundo (DL) han dominado la investigación del sistema de detección de intrusiones (IDS) en los últimos años. Desafortunadamente, muchos estudios existentes han producido en

La noticia

arXiv:2609.36039v1 Announce Type: new Abstract: 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 benchmark with corrected PCAP-level labeling and a complete