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.