  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.
