Are We Missing Labels? A Study of the Availability of Ground-Truth in Network Security Research
Sebastian Abt, Harald Baier · 2014
Network security is a long-lasting field of research constantly encountering new challenges. Inherently, research in this field is highly data-driven. Specifically, many approaches employ a supervised machine learning approach requiring labelled input data. While different publicly available data sets exist, labelling information is sparse. In order to understand how our community deals with this lack of labels, we perform a systematic study of network security research accepted at top IT security conferences in 2009-2013. Our analysis reveals that 70% of the papers reviewed rely on manually compiled data sets. Furthermore, only 10% of the studied papers release the data sets after compilation. This manifests that our community is facing a missing labelled data problem. In order to be able to address this problem, we give a definition and discuss crucial characteristics of the problem. Furthermore, we reflect and discuss roads towards overcoming this problem by establishing ground-truth and fostering data sharing.