【Author】
Lv, Pin; Xie, Linyan; Xu, Jia; Li, Taoshen
【Source】ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2021, PT III
【Abstract】As an irreversible trend, connected vehicles become increasingly more popular. They depend on the generation and sharing of data between vehicles to improve safety and efficiency of the transportation system. However, due to the open nature of the vehicle network, dishonest and misbehaving vehicles may exist in the vehicular network. Misbehavior detection has been studied using machine learning in recent years. Existing misbehavior detection approaches require network equipment with powerful computing capabilities to constantly train and update sophisticated network models, which reduces the efficiency of the misbehavior detection system due to limited resources and untimely model updates. In this paper, we propose a new federated learning scheme based on blockchain, which can reduce resource utilization while ensuring data security and privacy. Further, we also design a blockchain-based reward mechanism for participants by automatically executing smart contracts. Common data falsification attacks are studied in this paper, and the experimental results show that our proposed scheme is feasible and effective.
【Keywords】VANET; Federated learning; Blockchain; Smart contract; Misbehavior detection
【标题】基于联邦学习和区块链的 VANET 中的不当行为检测
【摘要】作为不可逆转的趋势，联网汽车越来越受欢迎。它们依赖于车辆之间数据的生成和共享，以提高运输系统的安全性和效率。然而，由于车辆网络的开放性，车辆网络中可能存在不诚实和行为不端的车辆。近年来，人们使用机器学习研究了不当行为检测。现有的不当行为检测方法需要具有强大计算能力的网络设备不断训练和更新复杂的网络模型，由于资源有限和模型更新不及时，从而降低了不当行为检测系统的效率。在本文中，我们提出了一种新的基于区块链的联邦学习方案，可以在保证数据安全和隐私的同时降低资源利用率。此外，我们还通过自动执行智能合约为参与者设计了基于区块链的奖励机制。本文研究了常见的数据篡改攻击，实验结果表明我们提出的方案是可行和有效的。
【关键词】车联网；联邦学习；区块链；智能合约；不当行为检测
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