A Comprehensive Analysis of Machine Learning Models for IDS

Intrusion detection systems (IDS) play a vital role in protecting computer networks from malicious activities. The effectiveness of IDS heavily relies on the quality and suitability of the datasets used for their training and evaluation. This paper conducts a comparative analysis of various datasets used in IDS research, considering factors such as size, diversity, data collection methods, annotation, availability, and reproducibility. The analysis aims to provide researchers and practitioners with valuable insights into the strengths and limitations of different datasets, enabling them to make informed decisions when selecting datasets for their specific research needs. Furthermore, benchmark datasets recognized in the IDS research community are identified to facilitate fair comparisons between different IDS models and techniques. The findings from this comparative analysis contribute to advancing IDS research by aiding in the selection of appropriate datasets, improving the reproducibility of research outcomes, and promoting the development of more effective intrusion detection systems.