Exploration of Mined Block Temporarily Holding and Enforce Fork Attacks by Selfish Mining Pool in Proof-of-Work Blockchain Systems

We explored mined block temporarily holding (MBTH), MBTH with enforce fork (MBTH-EF), and intermittent MBTH-EF (iMBTH-EF) attacks to understand the effect of selfish mining on winning rate and fairness from the pool operation perspective. The temporary holding time of the current mined block provides a pool additional time to begin mining the next block early with MBTH attack. In this situation, the winning probability increases for the next block, but the pool may risk losing the mined block. One enhanced method to maintain the winning probability of the held block is to ensure the holding pool sends out the mined block once it receives the mined block message from the other pools with the MBTH-EF attack. The explored MBTH attacks differ from the existing selfish miner and pool attacks. The operations and effects of the MBTH-EF attack are different from stubborn mining strategies and a self-holding attack integrates selfish and stubborn mining attacks. We propose a mining competition solution that does not involve actual hash calculation. It entails using one stochastic target hash value for batch racing simulation to evaluate the holding threshold, holding periods, mining time, mining difficulty, pool sizes, and the rate of fork occurrences according to the operation data of the Bitcoin system. Accordingly, the dynamic time-by-time, block-by-block, and pool-by-pool simulations are adopted to study these attacks. We analyzed MBTH and MBTH-EF attacks as well as evaluated the effects of the win rate on when and how long a block is held. Because the periodic adjustment of mining difficulty reduces the holding effect, we further evaluated how an intermittent MBTH-EF (iMBTH-EF) attack model balance the average mining time and mining difficulty according to the mining difficulty adjustment of a stage. The effect of intermittent holding is examined on the mining game win rate for a long-term competition. We also identified suitable attack detection methods for the future work according to the simulation results.