DEFENDER-ATTACKER MODELS FOR RESOURCE ALLOCATION IN INFORMATION SECURITY
DOI:
https://doi.org/10.37943/AITU.2021.96.94.001Keywords:
Game theory, defender-attackers model, one defender-two attackers model, resource allocation, social networks, Nash equilibrium, Stackelberg leadership model, optimal resource allocation, information securityAbstract
Today, information security in defender-attacker game models is getting more attention from the research community. A game-theoretic approach applied in resource allocation study requires security in information for successive defensive strategy against attackers. For the defensive side players, allocating resources effectively and appropriately is essential to maintain the winning position against the attacking side. It can be possible by making the best response to the attack, i.e., by defining the most effective secure defensive strategy. This present work develops one defender – two attackers game model to determine the defensive strategy based on the Nash equilibrium and Stackelberg leadership equilibrium solutions of one defender-one attacker game model. Both game models are designed and studied in two scenarios: simultaneous and sequential modes. Game modes are defined according to the information that is available for attackers. In the first one, the defender is not aware of the attack and makes a simultaneous decision of how many resources should be allocated. Meanwhile, in the second mode, the defender knows about the entrance of attackers into a market and is assumed to commit a better strategy. The budget constraints are studied for both modes, all calculations and proof are presented in the work. According to obtained game mathematical models, it can be highlighted that network value of customers is important through the introduction of new variables in modeling and performing game theory equilibriums. This paper underlines the importance of information availability, budget limitations, and network value of customers in resource allocation through mathematical models and proofs; and focuses on modeling and studying defender-attacker games to define defensive strategy.
References
Deng, Z., & Kong, Z. (2020). Multi-Agent Cooperative Pursuit-Defense Strategy Against One Single Attacker. IEEE Robotics And Automation Letters, 5(4), 5772-5778. https://doi.org/10.1109/lra.2020.3010740
Fu, J., Li, Z., Sun, D., & Liu, W. (2013, November). Modeling multiple locations defence and attack dynamic Bayesian decision game. In 2013 Sixth International Conference on Business Intelligence and Financial Engineering (pp. 449-453). IEEE.
Friedman, L. (1958). Game-Theory Models in the Allocation of Advertising Expenditures. Operations Research, 6(5), 699-709. https://doi.org/10.1287/opre.6.5.699
Ab Ghani, A. T., & Tanaka, K. (2011). Network Games with Many Attackers and Defenders. Proceedings of Research Institute for Mathematical Sciences (RIMS) Kôkyûroku Kyoto University, 1729, 146-151.
Korzhyk, D., Conitzer, V., & Parr, R. (2011, June). Security games with multiple attacker resources. In Twenty-Second International Joint Conference on Artificial Intelligence.
Lei, H., Huang, S., Liu, Y., & Zhang, T. (2019). Robust optimization for microgrid defense resource planning and allocation against multi-period attacks. IEEE Transactions on Smart Grid, 10(5), 5841-5850. https://doi.org/10.1109/tsg.2019.2892201
Li, Y., Xiao, L., Dai, H., & Poor, H. V. (2017, May). Game theoretic study of protecting MIMO transmissions against smart attacks. In 2017 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.
Masucci, A. M., & Silva, A. (2015, December). Defensive resource allocation in social networks. In 2015 54th IEEE Conference on Decision and Control (CDC) (pp. 2927-2932). IEEE.
Sahabandu, D., Moothedath, S., Allen, J., Clark, A., Bushnell, L., Lee, W., & Poovendran, R. (2019, December). Dynamic information flow tracking games for simultaneous detection of multiple attackers. In 2019 IEEE 58th Conference on Decision and Control (CDC) (pp. 567-574). IEEE.
Sanjab, A., & Saad, W. (2016). Data injection attacks on smart grids with multiple adversaries: A game-theoretic perspective. IEEE Transactions on Smart Grid, 7(4), 2038-2049. https://doi.org/10.1109/tsg.2016.2550218
Vejandla, P., Dasgupta, D., Kaushal, A., & Nino, F. (2010, August). Evolving gaming strategies for attacker-defender in a simulated network environment. In 2010 IEEE Second International Conference on Social Computing (pp. 889-896). IEEE.
Wang, C., Hou, Y., & Ten, C. W. (2016). Determination of Nash equilibrium based on plausible attackdefense dynamics. IEEE Transactions on Power Systems, 32(5), 3670-3680. https://doi.org/10.1109/tpwrs.2016.2635156
Xiang, Y., & Wang, L. (2018). An improved defender–attacker–defender model for transmission line defense considering offensive resource uncertainties. IEEE Transactions on Smart Grid, 10(3), 2534-2546. https://doi.org/10.1109/tsg.2018.2803783
Xu, Z., & Zhuang, J. (2019). A study on a sequential one-defender-N-attacker game. Risk Analysis, 39(6),1414-1432. https://doi.org/10.1111/risa.13257
Ye, M., & Hu, G. (2015). Distributed seeking of time-varying Nash equilibrium for non-cooperative games. IEEE Transactions on Automatic Control, 60(11), 3000-3005. https://doi.org/10.1109/tac.2015.2414817.
Yuan, H., Xia, Y., Zhang, J., Yang, H., & Mahmoud, M. S. (2019). Stackelberg-game-based defense analysis against advanced persistent threats on cloud control system. IEEE Transactions on Industrial Informatics, 16(3), 1571-1580. https://doi.org/10.1109/tii.2019.2925035
Zhang, J., & Zhuang, J. (2019). Modeling a multi-target attacker-defender game with multiple attack types. Reliability Engineering & System Safety, 185, 465-475. https://doi.org/10.1016/j.ress.2019.01.015
Zhang, J., Zhuang, J., & Jose, V. (2018). The role of risk preferences in a multi-target defenderattacker resource allocation game. Reliability Engineering & System Safety, 169, 95-104. https://doi.org/10.1016/j.ress.2017.08.002
Zhang, L., & Li, J. (2018). Enabling robust and privacy-preserving resource allocation in fog computing. IEEE Access, 6, 50384-50393. https://doi.org/10.1109/access.2018.2868920
Zhang, X., Hipel, K. W., Ge, B., & Tan, Y. (2019). A game-theoretic model for resource allocation with deception and defense efforts. Systems Engineering, 22(3), 282-291. https://doi.org/10.1002/sys.21479
Zhang, X., Li, X., & Yuan, Z. (2019, October). Defending a single object in a defender-attacker game considering time. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp.506-510). IEEE.
Zhu, Q., Bushnell, L., & Başar, T. (2012, December). Game-theoretic analysis of node capture and cloning attack with multiple attackers in wireless sensor networks. In 2012 IEEE 51st IEEE Conference on Decision and Control (CDC) (pp. 3404-3411). IEEE.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Articles are open access under the Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish a manuscript in this journal agree to the following terms:
- The authors reserve the right to authorship of their work and transfer to the journal the right of first publication under the terms of the Creative Commons Attribution License, which allows others to freely distribute the published work with a mandatory link to the the original work and the first publication of the work in this journal.
- Authors have the right to conclude independent additional agreements that relate to the non-exclusive distribution of the work in the form in which it was published by this journal (for example, to post the work in the electronic repository of the institution or publish as part of a monograph), providing the link to the first publication of the work in this journal.
- Other terms stated in the Copyright Agreement.