A graph-based cyber threat modeling and risk assessment framework for smart microgrid systems

Abstract

The integration of Internet of Things (IoT) technologies in smart microgrids (SMGs) improves monitoring and control capabilities but also increases exposure to cyber–physical threats. This paper proposes a graph-based cyber–physical threat modelling and quantitative risk assessment framework for smart microgrids that evaluates device criticality, attack propagation, and overall system risk. The microgrid is modelled as a heterogeneous directed graph in which devices and communication links are associated with vulnerability, dependency, and communication exposure attributes derived from CVE/CVSS information. Mathematical formulations are developed to define attack risk indicators, determine success rate of attacks, and its impact. The framework is evaluated using a 24-hour smart microgrid simulation, case study of cyber-attack scenarios including False Data Injection (FDI), and Denial-of-Service (DoS), followed by IEEE 33-bus distribution system validation. The proposed framework facilitates simulation of attacks on smart microgrid system and allows user to automatically visualize, attack outcomes such as attack success rate, associated cost, and its impact. It generates a risk profile along with propagation path for security-oriented planning of future microgrids and testing of new techniques even before they get implemented. The experimental analysis is conducted to show high-risk attack paths within the communication infrastructure, economic impact, resilience, and mitigation analyses. The proposed framework provides an effective decision-support tool for vulnerability assessment, risk prioritization, and cybersecurity planning in smart microgrid environments.