Since the beginning of this decade, several incidents report that false data injection attacks targeting intelligent connected vehicles cause huge industrial damage and loss of lives. Data Theft, Flooding, Fuzzing, Hijacking, Malware Spoofing and Advanced Persistent Threats have been immensely growing attack that leads to end-user conflict by abolishing trust on autonomous vehicle. Looking after those sensitive data that contributes to measure the localisation factors of the vehicle, conventional centralised techniques can be misused to update the legitimate vehicular status maliciously. As investigated, the existing centralized false data detection approach based on state and likelihood estimation has a reprehensible trade-off in terms of accuracy, trust, cost, and efficiency. Blockchain with Fuzzy-logic Intelligence has shown its potential to solve localisation issues, trust and false data detection challenges encountered by today's autonomous vehicular system. The proposed Blockchain-based fuzzy solution demonstrates a novel false data detection and reputation preservation technique. The illustrated proposed model filters false and anomalous data based on the vehicles' rules and behaviours. Besides improving the detection accuracy and eliminating the single point of failure, the contributions include appropriating fuzzy AI functions within the Road-side Unit node before authorizing status data by a Blockchain network. Finally, thorough experimental evaluation validates the effectiveness of the proposed model.

翻译：自本世纪初以来，多起事件报告表明，针对智能网联汽车的虚假数据注入攻击已造成巨大的工业损失和人员伤亡。数据窃取、泛洪攻击、模糊测试、劫持攻击、恶意软件欺骗以及高级持续性威胁等攻击手段日益猖獗，通过削弱对自动驾驶车辆的信任，最终引发用户冲突。对于用于测量车辆定位因素的敏感数据，传统集中式技术可能被恶意利用以篡改合法车辆状态。经研究，现有的基于状态和似然估计的集中式虚假数据检测方法在准确性、可信度、成本与效率之间存在着不可忽视的权衡。基于区块链与模糊逻辑的智能技术已展现出解决当前自动驾驶系统所面临的定位问题、信任挑战及虚假数据检测难题的潜力。本文提出的基于区块链的模糊解决方案，展示了一种新颖的虚假数据检测与信誉保持技术。所阐述的模型依据车辆规则与行为，过滤虚假及异常数据。除提升检测精度、消除单点故障外，本研究的贡献还包括：在区块链网络授权状态数据之前，将模糊人工智能功能嵌入路侧单元节点。最后，通过全面的实验评估验证了所提模型的有效性。