IEEE Transactions on Industrial Electronics · 2018 · 139 citations · 19 references
EngineeringPhysical ImpactIndustrial EngineeringInformation SecurityCyber-physical-social SystemsHardware SecurityAttack SimulationReliability EngineeringScada SecuritySystems EngineeringModeling And SimulationCps SecurityIndustrial InformaticsSecurity RiskComputer EngineeringBayesian NetworkComputer ScienceCyberattackCyber-physical Production SystemCyber Physical SystemsIndustrial Cyber-physical SystemsSmart GridControl System SecurityTechnology
Industrial cyber‑physical systems are critical to infrastructures such as chemical plants, water networks, and power grids, yet cyberattacks can inflict physical damage, making their security essential. The paper proposes a novel risk‑assessment method to quantify how cyberattacks affect the physical components of ICPSs. The method employs a Bayesian network to model attack propagation and infer compromised sensor and actuator probabilities, which are then input into a stochastic hybrid system to predict physical process evolution and evaluate system availability. Its effectiveness is shown through a hardware‑in‑the‑loop simulation case study.
Industrial cyber-physical systems (ICPSs) are widely applied in critical infrastructures such as chemical plants, water distribution networks, and power grids. However, they face various cyberattacks, 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 cyberattacks on the physical system of ICPSs. This method 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 test bed.
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Attacks against process control systems
Álvaro A. Cárdenas, Saurabh Amin, Zong-Syun Lin et al. · 2011 · 684 citations
Common Vulnerability Scoring System
Peter Mell, Karen Scarfone, Sasha Romanosky · IEEE Security & Privacy · 2006 · 662 citations