A Hypothesis Testing tool for the comparison of different Cyber-Security Mitigation Strategies in IoT

Internet of Things (IoT) is a field with tremendous growth that already shows great impact in numerous domains. Simultaneous with this development is the need for better Cyber-security: IoT systems are attacked by various adversaries targeting IoT services, platforms and networks, which can have disruptive consequences. These attacks can be countered using multiple strategies with different effects to the system. The following paper, proposes a novel approach based on Machine Learning and Statistical Hypothesis Testing, which allows the security operator to investigate how using different strategies affects various KPI related to the security of the IoT network and if the KPI resulting from modifications to a mitigation strategy are statistically different when compared to those occurring from a starting mitigation action set.