Abstract
The impressive growth of the IoT we witnessed in the recent years came together with a surge in cyber attacks that target it. Factories adhering to digital transformation programs are quickly adopting the IoT paradigm and are thus increasingly exposed to a large number of cyber threats that need to be detected, analyzed and appropriately mitigated. In this scenario, a common approach that is used in large organizations is to setup an attack triage system. In this setting, security operators can cherry-pick new attack patterns requiring further in-depth investigation from a mass of known attacks that can be managed automatically. In this paper, we propose an attack triage system that helps operators to quickly identify attacks with unknown behaviors, and later analyze them in detail. The novelty introduced by our solution is in the usage of process mining techniques to model known attacks and identify new variants. We demonstrate the feasibility of our approach through an evaluation based on three well-known IoT botnets, BASHLITE, LIGHTAIDRA and MIRAI, and on real current attack patterns collected through an IoT honeypot.