An Evolutionary Game Theory-Based Method to Mitigate Block Withholding Attack in Blockchain System

Abstract

Consensus algorithms are the essential components of blockchain systems. They guarantee the blockchain’s fault tolerance and security. The Proof of Work (PoW) consensus algorithm is one of the most widely used consensus algorithms in blockchain systems, using computational puzzles to enable mining pools to compete for block rewards. However, this excessive competition for computational power will bring security threats to blockchain systems. A block withholding (BWH) attack is one of the most critical security threats blockchain systems face. A BWH attack obtains the reward of illegal block extraction by replacing full proof with partial mining proof. However, the current research on the BWH game could be more extensive, considering the problem from the perspective of a static game, and it needs an optimal strategy that dynamically reflects the mining pool for multiple games. Therefore, to solve the above problems, this paper uses the method of the evolutionary game to design a time-varying dynamic game model through the degree of system supervision and punishment. Based on establishing the game model, we use the method of replicating dynamic equations to analyze and find the optimal strategy for mining pool profits under different BWH attacks. The experimental results demonstrate that the mining pools will choose honest mining for the best profit over time under severe punishment and high supervision. On the contrary, if the blockchain system is supervised with a low penalty, the mining pools will eventually choose to launch BWH attacks against each other to obtain the optimal mining reward. These experimental results also prove the validity and correctness of our model and solution.

Analyzing Miners’ Dynamic Equilibrium in Blockchain Networks under DDoS Attacks

“…Currently, there are security threats and hidden dangers in the blockchain system. Attacks such as selfish mining attacks, block interception attacks, and DDoS attacks exist in blockchain systems to illegally obtain mining rewards from honest-mining miners [5]. Selfish mining attacks are used to illegally gain access to the benefits of the public chain by creating a private chain outside of the public chain.…”

“…There has been some research on blockchain systems facing BWH attacks and selfish mining attacks in blockchain systems using dynamic games to solve the problem of decreasing their mining revenue. Huang et al [5] proposed a dynamic evolutionary game model to mitigate the loss of revenue from BWH attacks faced by mining pools in blockchain systems. This dynamic game model was able to obtain the optimal solution for mining pools facing BWH attacks with different penalties and rewards.…”

“…The malicious nodes will obtain an illegal block reward by launching several types of attacks, such as denial of service (DoS) attacks, blockchain DoS (BDoS) attacks, and distributed DoS (DDoS) attacks on blockchain networks [4]. The occurrence of such problems brings huge mining reward losses and security threats to blockchain networks [5]. Among them, DDoS attacks are the most important security threat [6].…”

Game Theory-Based Incentive Design for Mitigating Malicious Behavior in Blockchain Networks

“…Furthermore, Xiao Liu et al introduced a method leveraging evolutionary game theory [25] to mitigate block withholding attacks in blockchain systems. Their dynamic game model adapts to the system's degree of supervision and punishment, offering insights into optimal strategies for mining pools under various attack scenarios.…”