Adversary-Augmented Simulation to evaluate fairness on HyperLedger Fabric
<|reference_start|>Adversary-Augmented Simulation to evaluate fairness on HyperLedger Fabric: This paper presents a novel adversary model specifically tailored to distributed systems, aiming to assess the security of blockchain networks. Building upon concepts such as adversarial assumptions, goals, and capabilities, our proposed adversary model classifies and constrains the use of adversarial actions based on classical distributed system models, defined by both failure and communication models. The objective is to study the effects of these allowed actions on the properties of distributed protocols under various system models. A significant aspect of our research involves integrating this adversary model into the Multi-Agent eXperimenter (MAX) framework. This integration enables fine-grained simulations of adversarial attacks on blockchain networks. In this paper, we particularly study four distinct fairness properties on Hyperledger Fabric with the Byzantine Fault Tolerant Tendermint consensus algorithm being selected for its ordering service. We define novel attacks that combine adversarial actions on both protocols, with the aim of violating a specific client-fairness property. Simulations confirm our ability to violate this property and allow us to evaluate the impact of these attacks on several order-fairness properties that relate orders of transaction reception and delivery.<|reference_end|>
@article{mahe2024adversary-augmented,
    title={Adversary-Augmented Simulation to evaluate fairness on HyperLedger
  Fabric},
    author={Erwan Mahe, Rouwaida Abdallah, Sara Tucci-Piergiovanni, Pierre-Yves
  Piriou},
    journal={arXiv preprint arXiv:2403.14342},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14342},
    primaryClass={cs.CR cs.DC cs.MA}
}