Cyber-Resilience Evaluation of Cyber-Physical Systems

Cyber-Physical Systems (CPS) use computational resources to control physical processes and provide critical services. For this reason, an attack in these systems may have dangerous consequences in the physical world. Hence, cyber- resilience is a fundamental property to ensure the safety of the people, the environment and the controlled physical processes. In this paper, we present metrics to quantify the cyber-resilience level based on the design, structure, stability, and performance under the attack of a given CPS. The metrics provide reference points to evaluate whether the system is better prepared or not to face the adversaries. This way, it is possible to quantify the ability to recover from an adversary using its mathematical model based on actuators saturation. Finally, we validate our approach using a numeric simulation on the Tennessee Eastman control challenge problem.

[1]  N. Lawrence Ricker,et al.  Model predictive control of a continuous, nonlinear, two-phase reactor , 1993 .

[2]  Paulo Tabuada,et al.  Secure Estimation and Control for Cyber-Physical Systems Under Adversarial Attacks , 2012, IEEE Transactions on Automatic Control.

[3]  R. Kálmán On the general theory of control systems , 1959 .

[4]  Stephen Hailes,et al.  A distributed trust model , 1998, NSPW '97.

[5]  Fikret Sivrikaya,et al.  Time synchronization in sensor networks: a survey , 2004, IEEE Network.

[6]  Maria Letizia Corradini,et al.  Robust detection and reconstruction of state and sensor attacks for cyber-physical systems using sliding modes , 2017 .

[7]  Shreyas Sundaram,et al.  Resilient Asymptotic Consensus in Robust Networks , 2013, IEEE Journal on Selected Areas in Communications.

[8]  Tricha Anjali,et al.  Symmetric-Key Generation Protocol (SGenP) for Body Sensor Network , 2018, 2018 IEEE International Conference on Communications Workshops (ICC Workshops).

[9]  Igor Linkov,et al.  Resilience metrics for cyber systems , 2013, Environment Systems and Decisions.

[10]  R. E. Kalman,et al.  Contributions to the Theory of Optimal Control , 1960 .

[11]  Shreyas Sundaram,et al.  Distributed Function Calculation via Linear Iterative Strategies in the Presence of Malicious Agents , 2011, IEEE Transactions on Automatic Control.

[12]  Kyriakos G. Vamvoudakis,et al.  A Moving Target Defense Control Framework for Cyber-Physical Systems , 2020, IEEE Transactions on Automatic Control.

[13]  Leslie Lamport,et al.  The Byzantine Generals Problem , 1982, TOPL.

[14]  Tao Zhang,et al.  A Review of Industrial MIMO Decoupling Control , 2019, International Journal of Control, Automation and Systems.

[15]  Marie Weisz Process Control Designing Processes And Control Systems For Dynamic Performance , 2016 .

[16]  Adi Shamir,et al.  How to share a secret , 1979, CACM.

[17]  Enrico Zio,et al.  Resilience-Based Component Importance Measures for Critical Infrastructure Network Systems , 2016, IEEE Transactions on Reliability.

[18]  Andrzej Pelc,et al.  Dissemination of Information in Communication Networks - Broadcasting, Gossiping, Leader Election, and Fault-Tolerance , 2005, Texts in Theoretical Computer Science. An EATCS Series.

[19]  Nicanor Quijano,et al.  Mitigating Sensor Attacks Against Industrial Control Systems , 2019, IEEE Access.

[20]  Ana R. Cavalli,et al.  Reflective Attenuation of Cyber-Physical Attacks , 2019, CyberICPS/SECPRE/SPOSE/ADIoT@ESORICS.

[21]  Luca De Cicco,et al.  On the use of watermark-based schemes to detect cyber-physical attacks , 2017, EURASIP J. Inf. Secur..

[22]  Royce A. Francis,et al.  A metric and frameworks for resilience analysis of engineered and infrastructure systems , 2014, Reliab. Eng. Syst. Saf..

[23]  M. Fikar DECOUPLING CONTROL , 2011 .

[24]  Insup Lee,et al.  Attack-Resilient State Estimation for Noisy Dynamical Systems , 2017, IEEE Transactions on Control of Network Systems.

[25]  Ping Zhang,et al.  Detection of covert attacks on cyber-physical systems by extending the system dynamics with an auxiliary system , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[26]  Craig G. Rieger,et al.  Resilient control systems Practical metrics basis for defining mission impact , 2014, 2014 7th International Symposium on Resilient Control Systems (ISRCS).

[27]  Saman Zonouz,et al.  Cyber-Physical Resilience: Definition and Assessment Metric , 2019, IEEE Transactions on Smart Grid.

[28]  Lihua Xie,et al.  Towards a unified resilience analysis: State estimation against integrity attacks , 2016, 2016 35th Chinese Control Conference (CCC).

[29]  Amos Beimel,et al.  Secret-Sharing Schemes: A Survey , 2011, IWCC.

[30]  Geoff Barton,et al.  Process control: Designing processes and control systems for dynamic performance , 1996 .

[31]  Jairo Giraldo,et al.  Constraining Attacker Capabilities Through Actuator Saturation , 2017, 2018 Annual American Control Conference (ACC).

[32]  Audun Jøsang,et al.  The right type of trust for distributed systems , 1996, NSPW '96.

[33]  W. Marsden I and J , 2012 .

[34]  Brian K. Johnson,et al.  Power system protection and resilient metrics , 2015, 2015 Resilience Week (RWS).

[35]  Alan Fekete,et al.  Asymptotically optimal algorithms for approximate agreement , 1986, PODC '86.

[36]  Luca De Cicco,et al.  Adaptive control‐theoretic detection of integrity attacks against cyber‐physical industrial systems , 2018, Trans. Emerg. Telecommun. Technol..

[37]  Ernest F. Brickell,et al.  Some Ideal Secret Sharing Schemes , 1990, EUROCRYPT.