Towards reproducible cyber-security research through complex node automation

Performing cyber-security experiments is challenging as access to necessary data is limited, especially at large-scale. If data is available, sharing is typically not possible due to privacy concerns and contractual requirements. Hence, reproducibility of research and comparability of results is difficult. For a prevailing empirical domain of research, this is a methodological problem. To address this problem, in this paper we propose a data generation toolchain based on automation of complex nodes - cnaf. This system is better suited for performing cyber-security experiments than related work. Especially, as our approach explicitly welcomes and leverages complexity, cnaf is capable of generating realistic data sets.