Assessing the physical impact of cyber-attacks on industrial cyber-physical systems

Industrial Cyber-Physical systems (ICPSs) are widely applied in critical infrastructures such as chemical plants, water distribution networks, and power grid. However, they face various cyber-attacks, which may cause physical damage to these industrial facilities. Therefore, ensuring the security of ICPSs is of paramount importance. For this purpose, a new risk assessment method is presented in this paper to quantify the impact of cyber-attacks on the physical system of ICPSs. It helps carry out appropriate attack mitigation measures. The method uses a Bayesian network to model the attack propagation process and infers the probabilities of sensors and actuators to be compromised. These probabilities are fed into a stochastic hybrid system (SHS) model to predict the evolution of the physical process being controlled. Then, the security risk is quantified by evaluating the system availability with the SHS model. The effectiveness of the proposed method is demonstrated with a case study on a hardware-in-the-loop simulation testbed