Game theory attack pricing for mining pools in blockchain-based IoT

Abstract

The malicious mining pool can sacrifice part of its revenue to employ the computing power of blockchain network. The employed computing power carries out the pool mining attacks on the attacked mining pool. To realize the win-win game between the malicious mining pool and the employee, the paper proposes an Employment Attack Pricing Algorithm (EAPA) of mining pools in blockchain based on game theory. In the EAPA, the paper uses mathematical formulas to express the revenue of malicious mining pools under the employment attack, the revenue increment of malicious mining pools, and the revenue of the employee. It establishes a game model between the malicious mining pool and the employee under the employment attack. Then, the paper proposes an optimal computing power price selection strategy of employment attack based on model derivation. In the strategy, the malicious mining pool analyzes the conditions for the employment attack, and uses the derivative method to find the optimal utilization value of computing power, employees analyze the conditions for accepting employment, and use the derivative method to find the optimal reward value of computing power. Finally, the strategy finds the optimal employment computing power price to realize Nash equilibrium between the malicious mining pool and the employee under the current computing power allocation. The simulation results show that the EAPA could find the employment computing power price that realizes the win-win game between the malicious mining pool and the employee. The EAPA also maximizes the unit computing power revenue of employment and the unit computing power revenue of honest mining in malicious mining pool at the same time. The EAPA outperforms the state-of-the-art methods such as SPSUCP, DPSACP, and FPSUCP.

Keywords

Game theory / Blockchain / PoW / Mining pool / Employment attack

References

[1] J.D. Vyas, M. Han, L. Li, S. Pouriyeh, J.S. He,Integrating blockchain technology into healthcare, in:Proceedings of the 2020 ACM Southeast Conference, 2020, pp. 197-203.

[2] I. Yaqoob, K. Salah, R. Jayaraman, Y. Al-Hammadi, Blockchain for healthcare data management: opportunities, challenges, and future recommendations, Neural Comput. Appl. 1 (1) (2021) 1-16.

[3] Y. Chen, H. Chen, Y. Zhang, M. Han, M. Siddula, Z. Cai, A survey on blockchain systems: attacks, defenses and privacy preservation, High-Confidence. Comput. 2 (2) (2021) 1-20.

[4] Z. Liu, N.C. Luong, W. Wang, D. Niyato, P. Wang, Y.-C. Liang, D.I. Kim, A survey on blockchain: a game theoretical perspective, IEEE Access 7 (7) (2019) 47615-47643.

[5] Y. Liu, S. Xie, W. Zhang, J. Zhu, N. Wang, Research on time optimal profit maximization in social network, J. Front. Comput. Sci. Technol. 11 (11) (2017) 1723-1732.

[6] Y. Lu, The blockchain: state-of-the-art and research challenges, J. Ind. Inf. Integrat. 15 (15) (2019) 80-90.

[7] S. Cao, L.W. Cong, M. Han, Q. Hou, B. Yang, Blockchain architecture for auditing automation and trust building in public markets, Computer 53 (7) (2020) 20-28.

[8] W. Wang, D.T. Hoang, P. Hu, Z. Xiong, D. Niyato, P. Wang, Y. Wen, D.I. Kim, A survey on consensus mechanisms and mining strategy management in blockchain networks, IEEE Access 7 (7) (2019) 22328-22370.

[9] Y. Yuan, X. Ni, S. Zheng, F. Wang, Blockchain consensus algorithms: the state of the art and future trends, Acta Autmatica Sinica 44 (11) (2018) 2011-2022.

[10] D. Laufenberg, L. Li, H. Shahriar, M. Han, Developing a blockchain-enabled collaborative intrusion detection system: an exploratory study,in: Future of Information and Communication Conference, Springer, 2020, pp. 172-183.

[11] J.A. Kroll, I.C. Davey, E.W. Felten, The economics of bitcoin mining, or bitcoin in the presence of adversaries,in: Proceedings of WEIS, 2013, pp. 1-21.

[12] Y. Chen, H. Chen, M. Han, B. Liu, Q. Chen, Z. Ma, Z. Wang, Miner revenue optimization algorithm based on pareto artificial bee colony in blockchain network, EURASIP J. Wirel. Commun. Netw. 2021 (1) (2021) 1-28.

[13] J. Han, J. Zou, H. Jian, Q. Xu, Research on mining attacks in bitcoin, J. Cryptologic. Res. 5 (5) (2018) 470-483.

[14] R. Qin, Y. Yuan, F. Wang, Research on the selection strategies of blockchain mining pools, IEEE Trans. Comput. Soc. Syst. 5 (3) (2018) 748-757.

[15] D. Laufenberg, L. Li, H. Shahriar, M. Han,An architecture for blockchain-enabled collaborative signature-based intrusion detection system, in:Proceedings of the 20th Annual SIG Conference on Information Technology Education, 2019, 169-169.

[16] K. Li, Y. Liu, H. Wan, Y. Huang, A discrete-event simulation model for the bitcoin blockchain network with strategic miners and mining pool managers, Comput. Oper. Res. 134 (134) (2021) 1-16.

[17] H. Chen, Y. Chen, Z. Xiong, M. Han, Z. He, B. Liu, Z. Wang, Z. Ma, Prevention method of block withholding attack based on miners' mining behavior in blockchain, Appl. Intell. 2022 (2022) (2022) 1-19.

[18] K. Liu, X. Qiu, W. Chen, X. Chen, Z. Zheng, Optimal pricing mechanism for data market in blockchain-enhanced internet of things, IEEE Internet Things J. 6 (6)(2019) 9748-9761.

[19] Q. Wang, H. Zhao, Q. Wang, H. Cao, G.S. Aujla, H. Zhu, Enabling secure wireless multimedia resource pricing using consortium blockchains, Future Generat. Comput. Syst. 110 (2020) 696-707.

[20] Y. Jiang, K. Zhou, X. Lu, S. Yang, Electricity trading pricing among prosumers with game theory-based model in energy blockchain environment, Appl. Energy 271 (271) (2020) 1-16.

[21] S. Xia, F. Lin, Z. Chen, C. Tang, Y. Ma, X. Yu, A bayesian game based vehicle-to-vehicle electricity trading scheme for blockchain-enabled internet of vehicles, IEEE Trans. Veh. Technol. 69 (7) (2020) 6856-6868.

[22] Z. Xiong, S. Feng, W. Wang, D. Niyato, P. Wang, Z. Han, Cloud/fog computing resource management and pricing for blockchain networks, IEEE Internet Things J. 6 (3) (2018) 4585-4600.

[23] Z. Xiong, S. Feng, D. Niyato, P. Wang, Z. Han, Optimal pricing-based edge computing resource management in mobile blockchain, in: 2018 IEEE International Conference on Communications (ICC), IEEE, 2018, pp. 1-6.

[24] Z. Xiong, J. Kang, D. Niyato, P. Wang, H.V. Poor, Cloud/edge computing service management in blockchain networks: multi-leader multi-follower game-based admm for pricing, IEEE Trans. Services. Comput. 13 (2) (2019) 356-367.

[25] S. Guo, Y. Dai, S. Guo, X. Qiu, F. Qi, Blockchain meets edge computing: stackelberg game and double auction based task offloading for mobile blockchain, IEEE Trans. Veh. Technol. 69 (5) (2020) 5549-5561.

[26] Y. Wei, M. Xiao, N. Yang, S. Leng, Block mining or service providing: a profit optimizing game of the pow-based miners, IEEE Access 8 (8) (2020) 134800-134816.

[27] N. Zhao, H. Wu, Y. Chen, Coalition game-based computation resource allocation for wireless blockchain networks, IEEE Internet Things J. 6 (5) (2019) 8507-8518.

[28] R. Singh, A.D. Dwivedi, G. Srivastava, A. Wiszniewska-Matyszkiel, X. Cheng, A game theoretic analysis of resource mining in blockchain, Cluster Comput. 23 (3)(2020) 2035-2046.

[29] I. Eyal, The miner's dilemma, in: 2015 IEEE Symposium on Security and Privacy, IEEE, 2015, pp. 89-103.

[30] L. Luu, R. Saha, I. Parameshwaran, P. Saxena, A. Hobor, On power splitting games in distributed computation: the case of bitcoin pooled mining, in: 2015 IEEE 28th Computer Security Foundations Symposium, IEEE, 2015, pp. 397-411.

[31] C. Tang, Z. Yang, Z. Zheng, Z. Chen, L. xiang, Game dilemma analysis and optimization of pow consensus algorithm, Acta Autom. Sin. 43 (9) (2017) 1520-1531.

[32] D. Wu, X. Liu, X. Yan, R. Peng, G. Li, Equilibrium analysis of bitcoin block withholding attack: a generalized model, Reliab. Eng. Syst. Saf. 185 (185) (2019) 318-328.

[33] W. Li, M. Cao, Y. Wang, C. Tang, F. Lin, Mining pool game model and nash equilibrium analysis for pow-based blockchain networks, IEEE Access 8 (8) (2020) 101049-101060.

[34] Y. Wang, C. Tang, F. Lin, Z. Zheng, Z. Chen, Pool strategies selection in pow-based blockchain networks: game-theoretic analysis, IEEE Access 7 (7) (2019) 8427-8436.

[35] A.T. Haghighat, M. Shajari, Block withholding game among bitcoin mining pools, Future Generat. Comput. Syst. 97 (97) (2019) 482-491.

[36] X. Du, D. Li, K. Liang, A biform game approach to preventing block withholding attack of blockchain based on semi-cis value, Int. J. Comput. Intell. Syst. 12 (2)(2019) 1353-1360.

[37] Y. Chen, H. Chen, M. Han, B. Liu, Q. Chen, T. Ren, A novel computing power allocation algorithm for blockchain system in multiple mining pools under withholding attack, IEEE Access 8 (2020) 155630-155644.

[38] X. Liu, W. Wang, D. Niyato, N. Zhao, P. Wang, Evolutionary game for mining pool selection in blockchain networks, IEEE Wireless Commun. Lett 7 (5) (2018) 760-763.

[39] A. Asheralieva, D. Niyato, Learning-based mobile edge computing resource management to support public blockchain networks, IEEE Trans. Mobile Comput. 20 (3) (2019) 1092-1109.

[40] M. Han, Z. Li, J. He, D. Wu, Y. Xie, A. Baba,A novel blockchain-based education records verification solution, in:Proceedings of the 19th Annual SIG Conference on Information Technology Education, 2018, pp. 178-183.

[41] Y. Fan, Z. Jin, G. Shen, D. Hu, L. Shi, X. Yuan, Three-stage stackelberg game based edge computing resource management for mobile blockchain, Peer-to-Peer Networking and Applications 14 (3) (2021) 1431-1445.

[42] Y. Liu, S. Xie, Z. Zhong, J. Li, Q. Ren, Topic-interest based influence maximization algorithm in social networks, J. Comput. Res. Dev. 55 (11) (2018) 2406-2418.

[43] A. Singh, R.M. Parizi, M. Han, A. Dehghantanha, H. Karimipour, K.R. Choo, Public blockchains scalability: an examination of sharding and segregated witness,in: Blockchain Cybersecurity, Trust and Privacy, Springer, 2020, pp. 203-232.

[44] Y. Sompolinsky, A. Zohar, Secure high-rate transaction processing in bitcoin, in: International Conference on Financial Cryptography and Data Security, Springer, 2015, pp. 507-527.

[45] A.P. Joshi, M. Han, Y. Wang, A survey on security and privacy issues of blockchain technology, Math. Found. Comput. 1 (2) (2018) 121-147.

[46] J. Kang, Z. Xiong, D. Niyato, P. Wang, D. Ye, D.I. Kim, Incentivizing consensus propagation in proof-of-stake based consortium blockchain networks, IEEE Wireless Commun. Lett 8 (1) (2018) 157-160.

[47] D. Wu, X. Liu, X. Yan, R. Peng, G. Li, Equilibrium analysis of bitcoin block withholding attack: a generalized model, Reliab. Eng. Syst. Saf. 185 (185) (2019) 318-328.

[48] R. Qin, Y. Yuan, F. Wang, Research on the selection strategies of blockchain mining pools, IEEE Trans. Comput. Soc. Syst. 5 (3) (2018) 748-757.