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In our modern society, computerized or digital control systems have been used to reliably automate many of the industrial operations that we take for granted, from the power plant to the automobiles we drive. However, the complexity of these systems and how the designers integrate them, the roles and responsibilities of the humans that interact with the systems, and the cyber security of these highly networked systems has led to a new paradigm in research philosophy for next generation control systems. Resilient Control Systems consider all of these elements and those disciplines that contribute to a more effective design, such as cognitive psychology, computer science, and control engineering to develop interdisciplinary solutions. These solutions consider such things such as how to tailor the control system operating displays to best enable the user to make an accurate and reproducible response, how to design in cyber security protections such that the system defends itself from attack by changing its behaviors, and how to better integrate widely distributed computer control systems to prevent cascading failures that result in disruptions to critical industrial operations. In the context of cyber-physical systems, resilient control systems are an aspect that focuses on the unique interdependencies of a control system, as compared to information technology computer systems and networks, due to its importance in operating our critical industrial operations.

Introduction

Originally intended to provide a more efficient mechanism for controlling industrial operations, the development of digital control systems allowed for flexibility in integrating distributed sensors and operating logic while maintaining a centralized interface for human monitoring and interaction.[1] This ease of readily adding sensors and logic through software, which was once done with relays and isolated analog instruments, has led to wide acceptance and integration of these systems in all industries. However, these digital control systems have often been integrated in phases to cover different aspects of an industrial operation, connected over a network, and leading to a complex interconnected and interdependent system.[2] While the control theory applied is often nothing more than a digital version of their analog counterparts, the dependence of digital control systems upon the communications networks, has precipitated the need for cybersecurity due to potential effects on confidentiality, integrity and availability of the information.[3] To achieve resilience in the next generation of control systems, therefore, addressing the complex control system interdependencies, including the human systems interaction and cyber security, will be a recognized challenge.[4]

Defining resilience

Research in resilience engineering over the last decade has focused in two areas, organizational and information technology. Organizational resilience considers the ability of an organization to adapt and survive in the face of threats, including the prevention or mitigation of unsafe, hazardous or compromising conditions that threaten its very existence[5]. Information technology resilience has been considered from a number of standpoints [6]. Networking resilience has been considered as quality of service [7]. Computing has considered such issues as dependability and performance in the face of unanticipated changes [8]. However, based upon the application of control dynamics to industrial processes, functionality and determinism are primary considerations that are not captured by the traditional objectives of information technology. [9].

Considering the paradigm of control systems, one definition has been suggested that "Resilient control systems are those that tolerate fluctuations via their structure, design parameters, control structure and control parameters"[10]. However, this definition is taken from the perspective of control theory application to a control system. The consideration of the malicious actor and cyber security are not directly considered, which might suggest the definition, "an effective reconstitution of control under attack from intelligent adversaries," which was proposed.[11] However, this definition focuses only on resilience in response to an malicious actor. To consider the cyber-physical aspects of control system, a definition for resilience considers both benign and malicious human interaction, in addition to the complex interdependencies of the control system application [12].

The use of the term “recovery” has been used in the context of resilience, paralleling the response of a rubber ball to stay intact when a force is exerted on it and recover its original dimensions after the force is removed.[13] Considering the rubber ball in terms of a system, resilience could then be defined as its ability to maintain a desired level of performance or normalcy without irrecoverable consequences. While resilience in this context is based upon the yield strength of the ball, control systems require an interaction with the environment, namely the sensors, valves, pumps that make up the industrial operation. To be reactive to this environment, control systems require an awareness of its state to make corrective changes to the industrial process to maintain normalcy.[14] With this in mind, in consideration of the discussed cyber-physical aspects of human systems integration and cyber security, as well as other definitions for resilience at a broader critical infrastructure level,[15][16] the following can be deduced as a definition of a resilient control system:

"A resilient control system is one that maintains state awareness and an accepted level of operational normalcy in response to disturbances, including threats of an unexpected and malicious nature"[4]
RCS Architecture 09-50368

Considering the flow of a digital control system as a basis, a resilient control system framework can be designed. Referring to the left side of the figure, a resilient control system holistically considers the measures of performance or normalcy for the state space. At the center, an understanding of performance and priority provide the basis for an appropriate response by a combination of human and automation, embedded within a multi-agent, semi-autonomous framework. Finally, to the right, information must be tailored to the consumer to address the need and position a desirable response. Several examples or scenarios of how resilience differs and provides benefit to control system design are available in the literature. [17][12]

Areas Of resilience

Some primary tenants of resilience, as contrasted to traditional reliability, have presented themselves in considering an integrated approach to resilient control systems.[18][19].[20] These cyber-physical tenants complement the fundamental concept of dependable or reliable computing by characterizing resilience in regard to control system concerns, including design considerations that provide a level of understanding and assurance in the safe and secure operation of an industrial facility. These tenants are discussed individually below to summarize some of the challenges to address in order to achieve resilience.

Human systems

The benign human has an ability to quickly understand novel solutions, and provide the ability to adapt to unexpected conditions. This behavior can provide additional resilience to a control system,[21] but reproducibly predicting human behavior is a continuing challenge. The ability to capture historic human preferences can be applied to bayesian inference and bayesian belief networks, but ideally a solution would consider direct understanding of human state using sensors such as an EEG.[22][23] Considering control system design and interaction, the goal would be to tailor the amount of automation necessary to achieve some level of optimal resilience for this mixed initiative response.[24] Presented to the human would be that actionable information that provides the basis for a targeted, reproducible response.[25]

Cyber security

In contrast to the challenges of prediction and integration of the benign human with control systems, the abilities of the malicious actor (or hacker) to undermine desired control system behavior also create a significant challenge to control system resilience.[26] Application of dynamic probabilistic risk analysis used in human reliability can provide some basis for the benign actor.[27] However, the decidedly malicious intentions of an adversarial individual, organization or nation make the modeling of the human variable in both objectives and motives.[28] However, in defining a control system response to such intentions, the malicious actor looks forward to some level of recognized behavior to gain an advantage and provide a pathway to undermining the system. Whether performed separately in preparation for a cyber attack, or on the system itself, these behaviors can provide opportunity for a successful attack without detection. Therefore in considering resilient control system architecture, atypical designs that imbed active and passively implemented randomization of attributes, would be suggested to reduce this advantage.[29][30]

Complex networks and networked control systems

While much of the current critical infrastructure is controlled by a web of interconnected control systems, either architecture termed as distributed control systems (DCS) or supervisory control and data acquisition (SCADA), the application of control is moving toward a more decentralized state. In moving to a smart grid, the complex interconnected nature of individual homes, commercial facilities and diverse power generation and storage creates an opportunity and a challenge to ensuring that the resulting system is more resilient to threats.[31][32] The ability to operate these systems to achieve a global optimum for multiple considerations, such as overall efficiency, stability and security, will require mechanisms to holistically design complex networked control systems.[33][34] Multi-agent methods suggest a mechanism to tie a global objective to distributed assets, allowing for management and coordination of assets for optimal benefit and semi-autonomous, but constrained controllers that can react rapidly to maintain resilience for rapidly changing conditions.[35][36]

Examples of Resilient Control System Developments

1) When considering the current digital control system designs, the cyber security of these systems is dependent upon what is considered border protections, i.e., firewalls, passwords, etc. If a malicious actor compromised the digital control system for an industrial operation by a man-in-the-middle attack, data can be corrupted with the control system. The industrial facility operator would have no way of knowing the data has been compromised, until someone a security engineer recognized the attack was occurring. As operators are trained to provide an prompt, appropriate response to stabilize the industrial facility, there is a likelihood that the corrupt data would lead to the operator reacting to the situation and lead to a plant upset. In a resilient control system, as per the figure, cyber and physical data is fused to recognize anomalous situations and warn the operator. [37]

2) As our society becomes more automated for a variety of drivers, including energy efficiency, the need to implement ever more effective control algorithms naturally follow. However, advanced control algorithms are dependent upon data from multiple sensors to predict the behaviors of the industrial operation and make corrective responses. This type of system can become very brittle, insofar as any unrecognized degradation in the sensor itself can lead to incorrect responses by the control algorithm and potentially a worsened condition relative to the desired operation for the industrial facility. Therefore, implementation of advanced control algorithms in a resilient control system also requires the implementation of diagnostic and prognostic architectures to recognize sensor degradation, as well as failures with industrial process equipment associated with teh control algorithms. [38]

Notes

  1. M. Gopal, "Digital Control And State Variable Method," Tata McGraw-Hill, pp. 3-12, 2009.
  2. Rinaldi, Peerenboom & Kelly 2001, pp. 11–25.
  3. DHS staff 2005.
  4. 4.0 4.1 Rieger, Gertman & McQueen 2009.
  5. Hollnagel, Woods & Leveson 2006,[page needed].
  6. Trivedi, Dong & Ghosh 2009, pp. 74-77.
  7. Cholda 2009, pp. 11-19.
  8. Meyer 2009.
  9. Wang & Liu 2008,[page needed].
  10. Mitchell & Mannan 2006, pp. 39-45.
  11. Proceedings of the 1st International Symposium on Resilient Control Systems, Idaho Falls, ID, 2008, https://secure.inl.gov/isrcs2008/default.aspx 
  12. 12.0 12.1 Rieger 2010, pp. 64-71.
  13. S. Jackson, Architecting Resilient Systems: Accident Avoidance and Survival and Recovery from Disruptions, John Wiley, Hoboken, November, 2009
  14. W. L. Luyben, Process Modelling, Simulation and Control for Chemical Engineers, McGraw-Hill, August, 1989
  15. Critical Infrastructure Resilience: Final Report and Recommendations, National Infrastructure Advisory Council, Department of Homeland Security, 2009
  16. Analysis of Prototypical Jurisdiction & Infrastructure Critical Facility Resiliencies, Advanced Research Institute Virginia Polytechnic Institute and State University Arlington, Virginia, 2009
  17. HTGR Resilient Control System Strategy, September 2010
  18. Proceedings of the International Symposium on Resilient Control Systems, 2008-2011
  19. Lin, Sedigh & Hurson 2011, pp. 93-103.
  20. High-Confidence Medical Devices: Cyber-Physical Systems for 21st Century Health Care, Networking and Information Technology Research and Development (NITRD), February 2009
  21. E. Hollnagel, J. Pariès, D. Woods and J. Wreathall, Resilience Engineering in Practice, Ashgate, London, 2010
  22. M. Schrauf, M. Simon, E. Schmidt and W. Kincses, Assessing Drivers' Fatigue State under Real Traffic Conditions using EEG Alpha Spindles, Sixth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, June, 2011
  23. S. E. Kerick, K. Mcdowell, '’Understanding Brain, Cognition, and Behavior in Complex Dynamic Environments,5th International Conference on Foundations of Augmented Cognition, 2009
  24. N. Adam, DHS Workshop on Future Directions in Cyber-Physical Systems Security: Final Report, January, 2010
  25. E. Blasch, M. Pribilski, B. Daughtery, B. Roscoe, and J. Gunsett, “Fusion Metrics for Dynamic Situation Analysis,” Proc. SPIE 5429, April, 2004
  26. P. Verissimo, Challenges of Architecting Resilient Critical Information Infrastructures, presentation at ENISA-FORTH Summer School on Network and Information Security, September, 2008
  27. R.L. Boring, Reconciling resilience with reliability: The complementary nature of resilience engineering and human reliability analysis, 53rd Annual Meeting of the Human Factors and Ergonomics Society, pp. 1589-1593, October, 2009
  28. S. Redwine, Introduction to Modeling Tools for Software Security, DHS US-CERT Build Security In Website, February, 2007
  29. H. G. Goldman, Building Secure, Resilient Architectures for Cyber Mission Assurance, MITRE, 2010
  30. M. A. McQueen, W. F. Boyer, Deception used for Cyber Defense of Control Systems, 2nd IEEE Conference on Human System Interaction, Catania, Italy, May, 2009
  31. V. Vyatkin, G. Zhabelova and M. Ulieru, Toward Digital Ecologies:Intelligent Agent Networks Controlling Interdependent Infrastructures, 1st IEEE Conference on Smart Grid Communications, October, 2010
  32. Alderson and Doyle, ‘’Contrasting views of Complexity and their Implications for Network-Centric Infrastructures’’,Transactions of IEEE Systems, Man and Cybernetics, Special Issue on Cyber-Physical Ecosystems, July 2010.
  33. S.P. Meyn, Control Techniques for Complex Networks, Cambridge University Press, New York, NY, 2008
  34. A. A. Cardenas, S. Amin, and S. S. Sastry, ‘’ Secure control: Towards survivable cyber-physical systems’’, 28th International Conference on Distibuted Computing Systems Workshops, pp. 495-500, 2008
  35. S. D. J. McArthur et. al., “Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges,” IEEE Transactions on Power Systems, pp. 1743-1752, November, 2007
  36. S. D. J. McArthur et. al., “Multi-Agent Systems for Power Engineering Applications—Part II: Technologies, Standards, and Tools for Building Multi-agent Systems,” IEEE Transactions on Power Systems, pp. 1753- 1759, November, 2007
  37. Linda, O.*, M. Manic, T. R. McJunkin, "Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine," in Proc. IEEE Symposium on Resilience Control Systems (ISRCS 2011), Boise, Idaho, August 9 – 11 2011
  38. Kun Ji, Yan Lu, Linxia Liao, Zhen Song, and Dong Wei, "Prognostics Enabled Resilient Control for Model-based Building Automation Systems," Proceedings of Building Simulation 2011, 12th Conference of International Building Performance Simulation Association, Sydney, November, 2011.

References

  • Cholda, P.; Tapolcai, J.; Cinkler, T.; Wajda, K.; Jajszczyk ,A. (2009), "Quality of resilience as a network reliability characterization Tool", IEEE Network 23 (2): 11–19 
  • DHS staff (May 2005), Critical Infrastructure Protection, Department of Homeland Security Faces Challenges in Fulfilling Cybersecurity Responsibilities,GAO-05-434, US Government 
  • Hollnagel, E.; Woods, D. D.; Leveson, N (2006), Resilience Engineering: Concepts and Precepts, Aldershot Hampshire, UK: Ashgate Publishing 
  • Kuo, B. C. (June 1995), Digital Control Systems, Oxford University Press 
  • Lin, J.; Sedigh, S.; Hurson, A.R. (May 2011), An Agent-Based Approach to Reconciling Data Heterogeneity in Cyber-Physical Systems, 25th IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), pp. 93–103 
  • Meyer, J. F. (September 2009), Defining and Evaluating Resilience: A Performability Perspective, presentation at International Workshop on Performability Modeling of Computer and Communication Systems 
  • Mitchell, S. M.; Mannan, M. S (April 2006), "Designing Resilient Engineered Systems", Chemical Engineering Progress 102 (4): 39–45 
  • Rieger, C. G. (August 2010), Notional examples and benchmark aspects of a resilient control system, 3rd International Symposium on Resilient Control Systems, pp. 64–71 
  • Rinaldi, S. M.; Peerenboom, J. P.; Kelly, T. K. (December 2001), "Identifying, Understanding and Analyzing Critical Infrastructure Interdependencies", IEEE Control Systems Magazine: 11–25 
  • Trivedi, K. S.; Dong, S. K.; Ghosh, R. (December 2009), Resilience in Computer Systems and Networks, IEEE/ACM International Conference on Computer-Aided Design-Digest of Technical Papers, pp. 74–77 
  • Wang, F.Y.; Liu, D. (2008), Networked Control Systems: Theory and Applications, London, UK: Springer-Verlag 
  • Wei, D.; Ji, K. (August 2010), Resilient industrial control system (RICS): Concepts, formulation, metrics, and insights, 3rd International Symposium Resilient Control Systems (ISRCS), pp. 15–22 
  • Wing, J. (April 2008), Cyber-Physical Systems Research Charge, St Louis, Missouri: Cyber-Physical Systems Summit 
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