Revisiting Double-Spending Attacks on the Bitcoin Blockchain: New Findings

Abstract

Bitcoin is currently the cryptocurrency with the largest market share. Many previous studies have explored the security of Bitcoin from the perspective of blockchain mining. Especially on the double-spending attacks (DSA), some state-of-the-art studies have proposed various analytical models, aiming to understand the insights behind the double-spending attacks. However, we believe that advanced versions of DSA can be developed to create new threats for the Bitcoin ecosystem. To this end, this paper mainly presents a new type of double-spending attack named Adaptive DSA in the context of the Bitcoin blockchain, and discloses the associated insights. In our analytical model, the double-spending attack is converted into a Markov Decision Process. We then exploit the Stochastic Dynamic Programming (SDP) approach to obtain the optimal attack strategies towards Adaptive DSA. Through the proposed analytical model and the disclosed insights behind Adaptive DSA, we aim to alert the Bitcoin ecosystem that the threat of double-spending attacks is still at a dangerous level.