Manipulated Transaction Collision Attack on Execute-Order-Validate Blockchain
Ming Zhang, Wenhai Sun, Hui Li, Xiaofeng Wang, Zihao Yang, Chao Qu, Xiaoguang Li · IEEE Transactions on Dependable and Secure Computing · 2024
The Execute-Order-Validate blockchain enhances performance by allowing parallel transaction execution, yet it also introduces transaction conflicts that can cause state inconsistencies in the ledger. Previous research has focused on resolving conflicts under the assumption of the “good” intent of the senders. In this paper, we explore an unstudied scenario where a malicious user can intentionally generate transaction collisions to disrupt the service request of a target user to the underlying decentralized application (DApp). We call it manipulated transaction collision (MTC) attack. We overcome the challenges of identifying the conditions and best strategies to launch this targeted attack under various network settings. Our experiment results show that the MTC attack can effectively cause the victim to be continuously rejected by the blockchain, i.e., over 90% success rate in all tested cases on the Hyperledger Fabric blockchain. To combat this new threat, we first propose a machine-learning-assisted detection method that helps identify the adversarial behavior within massive background traffic. To further enhance blockchain resilience, we propose a more precise transaction conflicts definition and present a novel mitigation method, which not only prevents the attack but also significantly reduces the probability of natural conflicts by up to 75% in the tested DApp compared to state-of-the-art optimization methods.