Game Theoretic Honeypot Deployment in Smart Grid
1.1. Related Works & Motivation
2. System Model
3. One-Shot Game
3.1. Game Formulation
- The set of players , which includes the attacker and the defender, i.e.,
- The set of actions for each player, i.e, for the defender and for the attacker.
- The payoff functions for each player, i.e., and .
3.2. Solution of Game 1
3.3. Strategy Selection When NE Does Not Exist
4. Repeated Game with Uncertainty about the Type of Attacker
4.1. Game Formulation
- The set of players that includes the attacker and the defender, i.e., .
- The set of states of nature, denoted by .
- The types of the attacker, i.e., the set .
- The set of actions for each player, i.e, for the defender and for the attacker of type a and b, respectively.
- The expected payoff functions for each player, i.e., and .
- The belief about the type of the attacker.
- The history of the game at the t-th round.
4.2. Solution of Game 2 Given Updated Beliefs
4.3. Update of Belief
5. Simulation Results & Discussion
5.1. One-Shot Game
5.2. Max-Min Solution in the One-Shot Game
5.3. Repeated Game
Conflicts of Interest
- Cisco Annual Internet Report (2018–2023). White Paper. Available online: https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html (accessed on 13 June 2020).
- Littlefield, M. Putting Industrial Cyber Security at the Top of the CEO Agenda; LNS Research Library: Cambridge, MA, USA, 2017. [Google Scholar]
- Global Risks 2018: Insight Report; World Economic Forum: Geneva, Switzerland, 2018. Available online: http://www3.weforum.org/docs/WEF_GRR18_Report.pdf (accessed on 13 June 2020).
- The SPEAR Project. Available online: https://www.spear2020.eu/ (accessed on 13 June 2020).
- Spitzner, L. The Honeynet Project: Trapping the hackers. IEEE Secur. Priv. 2003, 1, 15–23. [Google Scholar] [CrossRef][Green Version]
- Spitzner, L. The Value of Honeypots, Part One: Definitions and Values of Honeypots. Available online: http://www.symantec.com/connect/articles/value-honeypots-part-one-definitions-and-values-honeypots (accessed on 13 June 2020).
- Scott, C.; Carbone, R. Designing and Implementing a Honeypot for a SCADA Network; SANS Institute Reading Room: Singapore, 2014; p. 39. [Google Scholar]
- Wei, L.; Sarwat, A.I.; Saad, W.; Biswas, S. Stochastic Games for Power Grid Protection Against Coordinated Cyber-Physical Attacks. IEEE Trans. Smart Grid 2018, 9, 684–694. [Google Scholar] [CrossRef]
- Pawlick, J.; Colbert, E.; Zhu, Q. A Game-theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy. ACM Comput. Surv. 2019, 52, 1–28. [Google Scholar] [CrossRef][Green Version]
- Tian, W.; Ji, X.; Liu, W.; Liu, G.; Zhai, J.; Dai, Y.; Huang, S. Prospect Theoretic Study of Honeypot Defense Against Advanced Persistent Threats in Power Grid. IEEE Access 2020, 8, 64075–64085. [Google Scholar] [CrossRef]
- Kumar, B.; Bhuyan, B. Using game theory to model DoS attack and defence. Sādhanā 2019, 44, 245. [Google Scholar] [CrossRef][Green Version]
- Çeker, H.; Zhuang, J.; Upadhyaya, S.; La, Q.D.; Soong, B.H. Deception-Based Game Theoretical Approach to Mitigate DoS Attacks. In Lecture Notes in Computer Science; Springer International Publishing: Berlin/Heidelberg, Germany, 2016; pp. 18–38. [Google Scholar]
- Wang, K.; Du, M.; Maharjan, S.; Sun, Y. Strategic Honeypot Game Model for Distributed Denial of Service Attacks in the Smart Grid. IEEE Trans. Smart Grid 2017, 8, 2474–2482. [Google Scholar] [CrossRef]
- Du, M.; Wang, K. An SDN-Enabled Pseudo-Honeypot Strategy for Distributed Denial of Service Attacks in Industrial Internet of Things. IEEE Trans. Ind. Inform. 2020, 16, 648–657. [Google Scholar] [CrossRef]
- Cho, J.H.; Zhu, M.; Singh, M. Modeling and Analysis of Deception Games Based on Hypergame Theory. In Auton. Cyber Decept.; Springer International Publishing: Berlin/Heidelberg, Germany, 2019; pp. 49–74. [Google Scholar]
- Horák, K.; Bošanský, B.; Tomášek, P.; Kiekintveld, C.; Kamhoua, C. Optimizing honeypot strategies against dynamic lateral movement using partially observable stochastic games. Comput. Secur. 2019, 87, 101579. [Google Scholar] [CrossRef]
- Tian, W.; Ji, X.; Liu, W.; Liu, G.; Lin, R.; Zhai, J.; Dai, Y. Defense Strategies Against Network Attacks in Cyber-Physical Systems with Analysis Cost Constraint Based on Honeypot Game Model. Comput. Mater. Contin. 2019, 60, 193–211. [Google Scholar] [CrossRef]
- Tian, W.; Ji, X.P.; Liu, W.; Zhai, J.; Liu, G.; Dai, Y.; Huang, S. Honeypot game-theoretical model for defending against APT attacks with limited resources in cyber-physical systems. ETRI J. 2019, 41, 585–598. [Google Scholar] [CrossRef]
- La, Q.D.; Quek, T.Q.S.; Lee, J.; Jin, S.; Zhu, H. Deceptive Attack and Defense Game in Honeypot-Enabled Networks for the Internet of Things. IEEE Int. Things J. 2016, 3, 1025–1035. [Google Scholar] [CrossRef]
- La, Q.D.; Quek, T.Q.S.; Lee, J. A game theoretic model for enabling honeypots in IoT networks. In Proceedings of the 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia, 22–27 May 2016; pp. 1–6. [Google Scholar]
- Bilinski, M.; Gabrys, R.; Mauger, J. Optimal Placement of Honeypots for Network Defense. In Lecture Notes in Computer Science; Springer International Publishing: Berlin/Heidelberg, Germany, 2018; pp. 115–126. [Google Scholar]
- Fraunholz, D.; Schotten, H.D. Strategic defense and attack in deception based network security. In Proceedings of the 2018 International Conference on Information Networking (ICOIN), Chiang Mai, Thailand, 10–12 January 2018; pp. 156–161. [Google Scholar]
- Jicha, A.; Patton, M.; Chen, H. SCADA honeypots: An in-depth analysis of Conpot. In Proceedings of the 2016 IEEE Conference on Intelligence and Security Informatics (ISI), Tucson, AZ, USA, 28–30 September 2016. [Google Scholar]
- Dalamagkas, C.; Sarigiannidis, P.; Ioannidis, D.; Iturbe, E.; Nikolis, O.; Ramos, F.; Rios, E.; Sarigiannidis, A.; Tzovaras, D. A Survey On Honeypots, Honeynets and Their Applications On Smart Grid. In Proceedings of the 2019 IEEE Conference on Network Softwarization (NetSoft), Paris, France, 24–28 June 2019. [Google Scholar]
- Islam, S.N.; Mahmud, M.; Oo, A. Impact of optimal false data injection attacks on local energy trading in a residential microgrid. ICT Express 2018, 4, 30–34. [Google Scholar] [CrossRef]
- Diamantoulakis, P.D.; Kapinas, V.M.; Karagiannidis, G.K. Big data analytics for dynamic energy management in smart grids. Big Data Res. 2015, 2, 94–101. [Google Scholar] [CrossRef][Green Version]
- Shafie, A.E.; Chihaoui, H.; Hamila, R.; Al-Dhahir, N.; Gastli, A.; Ben-Brahim, L. Impact of Passive and Active Security Attacks on MIMO Smart Grid Communications. IEEE Syst. J. 2019, 13, 2873–2876. [Google Scholar] [CrossRef]
- El Shafie, A.; Niyato, D.; Hamila, R.; Al-Dhahir, N. Impact of the Wireless Network’s PHY Security and Reliability on Demand-Side Management Cost in the Smart Grid. IEEE Access 2017, 5, 5678–5689. [Google Scholar] [CrossRef]
- Niyato, D.; Wang, P.; Hossain, E. Reliability analysis and redundancy design of smart grid wireless communications system for demand side management. IEEE Wirel. Commun. 2012, 19, 38–46. [Google Scholar] [CrossRef]
- Mohsenian-Rad, A.; Wong, V.W.S.; Jatskevich, J.; Schober, R.; Leon-Garcia, A. Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid. IEEE Trans. Smart Grid 2010, 1, 320–331. [Google Scholar] [CrossRef][Green Version]
- Iqbal, A.; Gunn, L.J.; Guo, M.; Ali Babar, M.; Abbott, D. Game Theoretical Modelling of Network/Cybersecurity. IEEE Access 2019, 7, 154167–154179. [Google Scholar] [CrossRef]
- Garg, N.; Grosu, D. Deception in Honeynets: A Game-Theoretic Analysis. In Proceedings of the 2007 IEEE SMC Information Assurance and Security Workshop, West Point, NY, USA, 20–22 June 2007; pp. 107–113. [Google Scholar]
- Liang, X.; Xiao, Y. Game Theory for Network Security. IEEE Commun. Surv. Tutor. 2013, 15, 472–486. [Google Scholar] [CrossRef][Green Version]
- Chamberlain, G. Econometric applications of maxmin expected utility. J. Appl. Econom. 2000, 15, 625–644. [Google Scholar] [CrossRef]
- Liu, Y.; Comaniciu, C.; Man, H. A Bayesian game approach for intrusion detection in wireless ad hoc networks. In Proceedings of the 2006 Workshop on Game Theory for Communications and Networks; Association for Computing Machinery: New York, NY, USA, 2006. [Google Scholar] [CrossRef]
- Diamond, S.; Boyd, S. CVXPY: A Python-embedded modeling language for convex optimization. J. Mach. Learn. Res. 2016, 17, 1–5. [Google Scholar]
- Agrawal, A.; Verschueren, R.; Diamond, S.; Boyd, S. A rewriting system for convex optimization problems. J. Control Decis. 2018, 5, 42–60. [Google Scholar] [CrossRef]
|strategy of the attacker for the i-th host|
|strategy of the defender for the i-th host|
|number of real devices|
|total number of available hosts|
|N||sum of connected real devices and honeypots|
|different terms’ weights of attacker’s payoffs|
|different terms’ weights of defender’s payoffs|
|portion of the number of hosts (N) that are honeypots|
|portion of the number of hosts (N) that are attacked|
|the maximum portion of the number of hosts (N) that are attacked|
|payoff of player i|
|set of players|
|set of actions for player i|
|y, ,||auxiliary variables|
|expected value of|
|probability of the event|
|a, b||the two types of attacker|
|attacker of type j|
|weight’s of attacker’s payoff when he is of type|
|weight’s of attacker’s payoff when he is of type|
|belief that the attacker is of type a|
|probability of attacking each host for the attacker of type i.|
|maximum value of the probability of attacking each host for the attacker of type i|
|states of the nature|
|t||round of the game in a repeated game|
|history of the game after t-th play|
|belongs to the NE|
|cost of under or over estimating the demand of the i-th device|
|the probability density function of the actual energy consumption|
|the mean energy demand of the i-th device|
|the maximum energy consumption|
|energy price in the unit commitment stage|
|energy price in the economic-dispatch stage|
|Random solutions for||2000|
|Random solutions for||2000|
|Random solutions for||2000|
|Number of rounds||50|
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Diamantoulakis, P.; Dalamagkas, C.; Radoglou-Grammatikis, P.; Sarigiannidis, P.; Karagiannidis, G. Game Theoretic Honeypot Deployment in Smart Grid. Sensors 2020, 20, 4199. https://doi.org/10.3390/s20154199
Diamantoulakis P, Dalamagkas C, Radoglou-Grammatikis P, Sarigiannidis P, Karagiannidis G. Game Theoretic Honeypot Deployment in Smart Grid. Sensors. 2020; 20(15):4199. https://doi.org/10.3390/s20154199Chicago/Turabian Style
Diamantoulakis, Panagiotis, Christos Dalamagkas, Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis, and George Karagiannidis. 2020. "Game Theoretic Honeypot Deployment in Smart Grid" Sensors 20, no. 15: 4199. https://doi.org/10.3390/s20154199