Evaluating Blockchain Protocols with Abusive Modeling.

Strategic evaluations of blockchain systems allow a better understanding of the security of the mining process. In recent years, many researchers have focused on developing optimal strategies to evaluate the impact of an adversary on the mining process using different attack situations such as selfish mining, double-spending, feather-forking, Denial of Service. These strategies rely on the use of the Markov Decision Process (MDP) to find optimal settings that an adversary can exploit to earn maximum profit in every round. However, these strategies do not consider a case where adversaries turn abusive, and their only aim is to harm the mining process without profit. Motivated by this, a self-defying adversary model is proposed that uses ZEBRA (Zero Expectation-Based Reward Abuse) strategy to cause a maximum impact on the rewards of the honest players at lower settings. With the proposed method, the adversary itself may not be profitable, but has better control over the chain growth and causes maximum damage to reward by delaying the blocks and inducing forks subject to its compliance degree. The evaluations are demonstrated to show the reward control by the adversary along with the impact on delays and forks, followed by the possibilities of attacks using the hashing powers of different mining pools.