Prevention method of block withholding attack based on miners' mining behavior in blockchain

【Author】 Chen, Hao; Chen, Yourong; Xiong, Zhenyu; Han, Meng; He, Zaobo; Liu, Banteng; Wang, Zhangquan; Ma, Zhenghua

【Source】APPLIED INTELLIGENCE

【影响因子】5.019

【Abstract】This paper proposes a prevention method of block withholding attack (PMBWA) based on miners' mining behavior in blockchain to prevent the block withholding attack. The PMBWA first performs the data pre-processing based on the box chart detection algorithm for data cleaning and preliminary verification. Then the PMBWA uses the behavior reward, punishment mechanism, and credit model to comprehensively evaluate the contribution of miners. The PMBWA proposes a credit level classification algorithm (CLCA) of miners that weighs posterior probability and similarity to detect the malicious miners. Finally, the PMBWA allocates the corresponding income weight for miners of different credit levels. The simulation results show that regardless of how the numbers of blocks and malicious computing power change, the PMBWA can allocate low-income weight to the corresponding malicious computing power, and significantly improve the precision rate and recall rate of malicious computing power detection in the defensive mining pool. The PMBWA can largely reduce the average cumulative income of malicious computing power and improve the average cumulative income of non-malicious computing power. The PMBWA outperforms the state-of-the-art methods such as ICIAS, SRIAS, and IASCM.

【Keywords】Blockchain; Block withholding attack; Proof of work; Malicious miner detection

【发表时间】

【收录时间】2022-08-28

【文献类型】实验仿真

【主题类别】

区块链治理-技术治理-挖矿检测