Single Plant Protection: A Game-Theoretical Model for Improving Chemical Plant Protection

In this chapter, we introduce a game theoretic model for protecting a chemical plant from intelligent attackers. The model is named Chemical Plant Protection Game, abbreviated as “CPP Game” [1]. The CPP Game is developed based on the general intrusion detection approach in chemical plants. To this end, the general intrusion detection approach is firstly introduced. We develop and explain the CPP Game by modelling its players, strategies, and payoffs. Afterwards in Sect. 3.3, different equilibrium concepts are used to predict the outcome of the CPP Game [2]. An analysis of the inputs and outputs of the game is provided in Sect. 3.4, from an industrial practice point of view [3]. Finally, conclusions are drawn at the end of this chapter.

[1]  Vicki M. Bier,et al.  Balancing Terrorism and Natural Disasters - Defensive Strategy with Endogenous Attacker Effort , 2007, Oper. Res..

[2]  Kenneth Sörensen,et al.  MISTRAL: A game-theoretical model to allocate security measures in a multi-modal chemical transportation network with adaptive adversaries , 2015, Reliab. Eng. Syst. Saf..

[3]  Sarit Kraus,et al.  Playing games for security: an efficient exact algorithm for solving Bayesian Stackelberg games , 2008, AAMAS.

[4]  Derek J. Clark,et al.  Contest success functions: an extension , 1998 .

[5]  Genserik Reniers,et al.  Applying a Bayesian Stackelberg game for securing a chemical plant , 2018 .

[6]  Paul Baybutt,et al.  Issues for security risk assessment in the process industries , 2017 .

[7]  S. Skaperdas.  Contest success functions , 1996 .

[8]  Genserik Reniers,et al.  Operational Safety Economics: A Practical Approach focused on the Chemical and Process Industries , 2016 .

[9]  Gerald G. Brown,et al.  Defending Critical Infrastructure , 2006, Interfaces.

[10]  Bin Chen,et al.  Integrating the API SRA methodology and game theory for improving chemical plant protection , 2018 .

[11]  C. E. Lemke,et al.  Equilibrium Points of Bimatrix Games , 1964 .

[12]  David A. Moore.  Security Risk Assessment Methodology for the petroleum and petrochemical industries , 2013 .

[13]  Vincent Conitzer,et al.  Computing the optimal strategy to commit to , 2006, EC '06.

[14]  J. Vial,et al.  Strategically zero-sum games: The class of games whose completely mixed equilibria cannot be improved upon , 1978 .

[15]  Jun Zhuang,et al.  Modeling Resources Allocation in Attacker-Defender Games with "Warm Up" CSF. , 2016, Risk analysis : an official publication of the Society for Risk Analysis.

[16]  Nicola Basilico,et al.  Computing Bayes-Nash Equilibria through Support Enumeration Methods in Bayesian Two-Player Strategic-Form Games , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[17]  Yoav Shoham,et al.  Multiagent Systems - Algorithmic, Game-Theoretic, and Logical Foundations , 2009 .

[18]  David K. Y. Yau,et al.  Defense of Cyber Infrastructures Against Cyber‐Physical Attacks Using Game‐Theoretic Models , 2016, Risk analysis : an official publication of the Society for Risk Analysis.

[19]  Genserik Reniers,et al.  A Game-Theoretical Model to Improve Process Plant Protection from Terrorist Attacks. , 2016, Risk analysis : an official publication of the Society for Risk Analysis.