A design-centric modeling approach was proposed to model the behavior of the physical process controlled by an Industrial Control System (ICS) and study the cascading effects of data-oriented attacks. A threat model was used as input to guide the construction of the model where control components which are within the adversary's intent and capabilities are extracted. The relevant control components are subsequently modeled together with their control dependencies and operational design specifications. The approach was demonstrated and validated on a water treatment testbed. Attacks were simulated on the testbed model where its resilience to attacks was evaluated using proposed metrics such as Impact Ratio and Time-to-Critical-State. From the analysis of the attacks, design strengths and weaknesses were identified and design improvements were recommended to increase the testbed's resilience to attacks.
In @cite_15 , Orojloo and Azgomi proposed a modeling approach that considers the systems dynamics and control dependencies between the various components in the cyber-physical systems. The model built was used to perform sensitivity analysis to understand the system behaviour under various attack scenarios, providing insights to vulnerable control loops. The impact on system's physical parameters by attacks on specific components on the system was subsequently used to evaluate the component's criticality for successful attacks.
@cite_15
2509307846 2787271297 2521497315 2963459078
Abstract Estimating the possible impacts of security attacks on physical processes can help to rank the critical assets based on their sensitivity to performed attacks and predict their attractiveness from the attacker’s point of view. To address this challenge, this paper proposes a new method for assessing the direct and indirect impacts of attacks on cyber–physical systems (CPSs). The proposed method studies the dynamic behavior of systems in normal situation and under security attacks and evaluates the consequence propagation of attacks. The inputs to the model are control parameters including sensor readings and controller signals. The output of the model is evaluating the consequence propagation of attacks, ranking the important assets of systems based on their sensitivity to conducted attacks, and prioritizing the attacks based on their impacts on the behavior of system. The validation phase of the proposed method is performed by modeling and evaluating the consequence propagation of attacks against a boiling water power plant (BWPP). In this paper, we design a secure, robust and resilient cyber-physical wind energy system (WES) from a cross-layer perspective. We establish a system-of-systems framework for the large-scale wind farm which includes the cyber and physical components. For the cyber layer, we use a game-theoretic model to capture the strategic behaviors of the network defender and the cyber attacker, and compute its mixed strategy Nash equilibria. For the physical layer, we design a robust and resilient switching controller using a Markov jump linear system model. Due to the interdependence between the cyber and physical systems, their performances are coupled and need to be designed in a holistic manner. To address this challenge, we propose an iterative algorithm to study the system-of-systems performance under the equilibrium design which jointly takes the cyber and physical layers into account. Case studies are provided to illustrate the interdependent design principles of cyber-physical WES. In order to accurately predict future states of a smart cyber-physical system, which can change its behavior to a large degree in response to environmental influences, the existence of precise models of the system and its surroundings is demandable. In machine engineering, ultra-high fidelity simulations have been developed to better understand both constraints in system design and possible consequences of external influences during the system's operation. These digital twins enable further applications in software design for complex cyber-physical systems as online planning methods can utilize good simulations to continuously optimize the system behavior, yielding a software architecture framework based on the information flow between the cyber-physical system, its physical environment and the digital twin model. Future power networks will be characterized by safe and reliable functionality against physical and cyber attacks. This paper proposes a unified framework and advanced monitoring procedures to detect and identify network components malfunction or measurements corruption caused by an omniscient adversary. We model a power system under cyber-physical attack as a linear time-invariant descriptor system with unknown inputs. Our attack model generalizes the prototypical stealth, (dynamic) false-data injection and replay attacks. We characterize the fundamental limitations of both static and dynamic procedures for attack detection and identification. Additionally, we design provably-correct (dynamic) detection and identification procedures based on tools from geometric control theory. Finally, we illustrate the effectiveness of our method through a comparison with existing (static) detection algorithms, and through a numerical study.