A timed automata-based approach for modeling and risk assessment of industrial robot systems

Industrial robot systems (IRSs) exhibit strong cyber-physical coupling, which traditional risk assessment methods fail to capture accurately because they lack the capability to model interconnected components and state transition constraints. This discrepancy leads to substantial deviations between assessment results and actual system behavior, thereby undermining the effectiveness of security evaluations. This paper presents a timed automata-based approach for modeling and risk assessment of IRSs. Based on analyses of the system’s network and functional characteristics, physical and cyber state models are constructed using timed automata. These models are then integrated via state-correlation analysis, which addresses the limitations arising from independently built models. In addition, a weighted bidirectional PageRank method is introduced to assess risks by quantifying both normal and risk states, while incorporating state-transition weights. To validate the proposed approach, a model of an IRS in a printed circuit board automated production line was developed using Uppsala Aalborg, a model-checking environment that supports timed-automata modeling and formal verification, and a corresponding risk assessment was performed. Results show that the model effectively simulates system operations and risk exploitation processes, with risk distribution patterns closely matching real-world observations. These findings demonstrate the efficacy and practical value of the proposed methodology in improving the accuracy of IRS risk assessments.