Introducing a Comprehensive, Continuous, and Collaborative Survey of Intrusion Detection Datasets

TL;DR

COMIDDS is a continuously updated, detailed, and collaborative online survey of intrusion detection datasets designed to help researchers select appropriate datasets and understand their limitations for more reliable experimental evaluations.

Contribution

It introduces a comprehensive, regularly updated online survey platform with detailed dataset information, addressing gaps in previous surveys and supporting better research decisions.

Findings

Provides structured, detailed dataset information including data samples and references.

Enables quick dataset selection based on specific research requirements.

Facilitates community contributions and ongoing updates to dataset evaluations.

Abstract

Researchers in the highly active field of intrusion detection largely rely on public datasets for their experimental evaluations. However, the large number of existing datasets, the discovery of previously unknown flaws therein, and the frequent publication of new datasets make it hard to select suitable options and sufficiently understand their respective limitations. Hence, there is a great risk of drawing invalid conclusions from experimental results with respect to detection performance of novel methods in the real world. While there exist various surveys on intrusion detection datasets, they have deficiencies in providing researchers with a profound decision basis since they lack comprehensiveness, actionable details, and up-to-dateness. In this paper, we present COMIDDS, an ongoing effort to comprehensively survey intrusion detection datasets with an unprecedented level of detail, implemented as a website backed by a public GitHub repository. COMIDDS allows researchers to quickly identify suitable datasets depending on their requirements and provides structured and critical information on each dataset, including actual data samples and links to relevant publications. COMIDDS is freely accessible, regularly updated, and open to contributions.