A Plea for Utilising Synthetic Data when Performing Machine Learning Based Cyber-Security Experiments.

ABSTRACTCyber-security research is a challenging venture where researchers especially face the problem of not having broad access to labelled real-world data sets. This unavailability of data challenges performing scientific sound experiments. Especially, for machine learning based systems this unavailability effectively hinders us to assess performance, attributes and limitations of such systems. One approach to address this lack of publicly available data is to perform experiments using synthetic data. However, we experience that synthetic data is seldom used in our community. This position paper gives a plea for utilising synthetic data when performing machine learning based cyber-security experiments. For this, we collect major challenges our community faces today and discuss how synthetic data can help solving them. Furthermore, we discuss open questions in the area of data synthesis and propose directions for future work.