Selfish mining, originally discovered by Eyal et al. , is a well-known attack where a selfish miner, under certain conditions, can gain a disproportionate share of reward by deviating from the honest behavior. In this paper, we expand the mining strategy space to include novel "stubborn" strategies that, for a large range of parameters, earn the miner more revenue. Consequently, we show that the selfish mining attack is not (in general) optimal. Further, we show how a miner can further amplify its gain by non-trivially composing mining attacks with network-level eclipse attacks. We show, surprisingly, that given the attacker's best strategy, in some cases victims of an eclipse attack can actually benefit from being eclipsed!
|Original language||English (US)|
|State||Published - Mar 2016|
|Event||2016 IEEE European Symposium on Security and Privacy (EuroS&P) - Saarbrucken, Germany|
Duration: Mar 21 2016 → Mar 24 2016
|Conference||2016 IEEE European Symposium on Security and Privacy (EuroS&P)|
|Period||3/21/16 → 3/24/16|
- online banking
- peer-to-peer computing
- computational modeling
Nayak, K., Kumar, S., Miller, A. E., & Shi, E. (2016). Stubborn Mining: Generalizing Selfish Mining and Combining with an Eclipse Attack. 305-320. Paper presented at 2016 IEEE European Symposium on Security and Privacy (EuroS&P), Saarbrucken, Germany.