Evaluating fork after withholding (FAW) attack in Bitcoin
- 17 May 2022
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
Abstract
Fork after withholding (FAW) attack is an easy-to-conduct attack in the Bitcoin system and it is hard to be detected than some attacks like selfish mining and selfholding attacks. The previous studies about FAW attack made some strong assumptions, such as no propagation delay in the network. This paper aims to quantitatively examine the profitability of FAW attack in Bitcoin system with block propagation delay. We first establish a novel analytic model, which can analyze FAW attack in the Bitcoin system. Then we apply the model to design metric formulas for the Bitcoin system. These formulas can be used to evaluate the miner profitability (in terms of miner reward) and the impact of FAW attack on system throughput (in terms of transactions per second). We make a comparison of FAW attack and other attacks (including selfish mining and selfholding attacks). Experimental results reveal that FAW adversaries can get more rewards in the network with propagation delay than without delay. The results of the comparison of selfish mining and FAW attacks show that adversaries with large computational power can conduct selfish mining or selfholding attack to get more rewards, but they can conduct FAW attack to profit more when their computational power is small. Our work can be used to analyze Bitcoin-like blockchain systems and help design and evaluate security mechanisms.Keywords
Funding Information
- Beijing Municipal Natural Science Foundation (M22037)
- Natural Science and Engineering Research Council (NSERC) of Canada
This publication has 4 references indexed in Scilit:
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- Understanding Selfish Mining in Imperfect Bitcoin and Ethereum Networks With Extended ForksIEEE Transactions on Network and Service Management, 2021
- Assessing blockchain selfish mining in an imperfect network: Honest and selfish miner viewsComputers & Security, 2020
- Selfholding: A combined attack model using selfish mining with block withholding attackComputers & Security, 2019