SURE: A Modeling and Simulation Integration Platform for Evaluation of Secure and Resilient Cyber–Physical Systems

Koutsoukos, Xenofon; Karsai, Gábor; Lászka, Áron; Neema, Himanshu; Potteiger, Bradley; Völgyesi, Péter; Vorobeychik, Yevgeniy; Sztipanovits, János

doi:10.1109/jproc.2017.2731741

Cited by 66 publications

(27 citation statements)

References 60 publications

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“…We designed and implemented the PRMWFA-ACPS with Ptolemy, which was developed by Berkeley University in the United States [40]- [41]. We used the role-oriented and model stratification to design the PRMWFA-ACPS, and we applied ports to complete the system construction and data communication.…”

Section: Prmwfa-cps Design In Ptolemymentioning

confidence: 99%

Precision Regulation Model of Water and Fertilizer for Alfalfa Based on Agriculture Cyber-Physical System

Liu

Zhang

et al. 2020

IEEE Access

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The regulation of water and fertilizer for alfalfa growth is not precise enough because the regulation strategy cannot track alfalfa growth dynamically. In this paper, we propose a precision regulation model of water and fertilizer for alfalfa based on agriculture cyber-physical system (ACPS) for irrigation and fertilizer management in alfalfa (PRMWFA-ACPS). The proposed PRMWFA-ACPS is a comprehensive model that includes the biophysical submodel, the computation submodel of water and fertilizer regulation, and the interaction of the submodels for both. The proposed model interacts with the alfalfa growth and its physical environment along with the irrigation strategy to improve the precise regulation of water and fertilizer. To verify the performance of the proposed model, we develop a simulation platform for PRMWFA-ACPS based on Ptolemy. Through physical experiments performed in the field at the Ningxia irrigation area of the Yellow River over three years (2016-2018), we verified and analyzed PRMWFA-ACPS by comparing the simulated and measured values, such as the growth period, leaf area index, soil water content and alfalfa yield. The experimental results show that the mean relative error of the growth period simulated by the model is between 1.9% and 6.8%, the mean relative error of the leaf area index simulated by the model is between 2.1% and 9.8%, the mean relative error of the soil water content simulated by the model is between 4.3% and 12.8%, and the mean relative error of the yield simulated by the model is between 1.2% and 14.3%. These findings indicate that PRMWFA-ACPS has promising applicability to the Ningxia irrigation area of the Yellow River and improves the accurate regulation of water and fertilizer application to alfalfa in a complex physical environment. INDEX TERMS Alfalfa, agriculture cyber-physical system, precise regulation, growth model, Ptolemy.

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Section: Prmwfa-cps Design In Ptolemymentioning

confidence: 99%

Precision Regulation Model of Water and Fertilizer for Alfalfa Based on Agriculture Cyber-Physical System

Liu

Zhang

et al. 2020

IEEE Access

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“…Model and tool integration, as well as separation of concerns, are important CPS research topics that have been addressed in very recent related work. Among those, [33] describes an integrated design and simulation platform for evaluating the resilience of a transportation network to cyber-attacks. Similarly, [34] discusses OpenMETA, an experimental design automation tool suite in the automotive domain.…”

Section: Related Workmentioning

confidence: 99%

Automated fault tolerance augmentation in model-driven engineering for CPS

Bertolotti

Navet

et al. 2020

Computer Standards & Interfaces

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Cyber-Physical Systems are usually subject to dependability requirements such as safety and reliability constraints. Over the last 50 years, a body of efficient fault-tolerance mechanisms has been devised to handle faults occurring at run-time. However, properly implementing those mechanisms is a time-consuming task that requires a great deal of know-how. In this paper, we propose a general framework which allows system designers to decouple functional and non-functional concerns, and express nonfunctional properties at design time using domain-specific languages. In the spirit of generative programming, functional models are then automatically "augmented" with dependability mechanisms. Importantly, the real-time behavior of the initial models in terms of sampling times and meeting deadlines is preserved. The practicality of the approach is demonstrated with the automated implementation of one prominent software fault-tolerance pattern, namely N-Version Programming, in the CPAL model-driven engineering workflow.

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“…The integration and connectivity between various system components allow data exchange and information processing to fine tune system processes. However, this integration and connectivity also opens new threat channels in the form of cyber-and cyber-physical attacks, against which these systems need to be secured [16], [17]. Conventional cybersecurity mechanisms are inadequate and thus need to be expanded to incorporate the complexity and physical aspects of such systems [16], [17], [18].…”

Section: Related Workmentioning

confidence: 99%

Synergistic Security for the Industrial Internet of Things: Integrating Redundancy, Diversity, and Hardening

Lászka¹,

Abbas²,

Vorobeychik³

et al. 2018

2018 IEEE International Conference on Industrial Internet (ICII)

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As the Industrial Internet of Things (IIot) becomes more prevalent in critical application domains, ensuring security and resilience in the face of cyber-attacks is becoming an issue of paramount importance. Cyber-attacks against critical infrastructures, for example, against smart water-distribution and transportation systems, pose serious threats to public health and safety. Owing to the severity of these threats, a variety of security techniques are available. However, no single technique can address the whole spectrum of cyber-attacks that may be launched by a determined and resourceful attacker. In light of this, we consider a multi-pronged approach for designing secure and resilient IIoT systems, which integrates redundancy, diversity, and hardening techniques. We introduce a framework for quantifying cyber-security risks and optimizing IIoT design by determining security investments in redundancy, diversity, and hardening. To demonstrate the applicability of our framework, we present two case studies in water distribution and transportation systems. Our numerical evaluation shows that integrating redundancy, diversity, and hardening can lead to reduced security risk at the same cost.

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SURE: A Modeling and Simulation Integration Platform for Evaluation of Secure and Resilient Cyber–Physical Systems

Cited by 66 publications

References 60 publications

Precision Regulation Model of Water and Fertilizer for Alfalfa Based on Agriculture Cyber-Physical System

Precision Regulation Model of Water and Fertilizer for Alfalfa Based on Agriculture Cyber-Physical System

Automated fault tolerance augmentation in model-driven engineering for CPS

Synergistic Security for the Industrial Internet of Things: Integrating Redundancy, Diversity, and Hardening

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