Trust management is an important security approach for the successful implementation of Vehicular Ad Hoc Networks (VANETs). Trust models evaluate messages to assign reward or punishment. This can be used to influence a driver's future behaviour. In the author's previous work, a sender side based trust management framework is developed which avoids the receiver evaluation of messages. However, this does not guarantee that a trusted driver will not lie. These "untrue attacks" are resolved by the RSUs using collaboration to rule on a dispute, providing a fixed amount of reward and punishment. The lack of sophistication is addressed in this paper with a novel fuzzy RSU controller considering the severity of incident, driver past behaviour, and RSU confidence to determine the reward or punishment for the conflicted drivers. Although any driver can lie in any situation, it is expected that trustworthy drivers are more likely to remain so, and vice versa. This behaviour is captured in a Markov chain model for sender and reporter drivers where their lying characteristics depend on trust score and trust state. Each trust state defines the driver's likelihood of lying using different probability distribution. An extensive simulation is performed to evaluate the performance of the fuzzy assessment and examine the Markov chain driver behaviour model with changing the initial trust score of all or some drivers in Veins simulator. The fuzzy and the fixed RSU assessment schemes are compared, and the result shows that the fuzzy scheme can encourage drivers to improve their behaviour.

翻译：信任管理是成功实现车载自组织网络（VANETs）的重要安全方法。信任模型通过评估消息来分配奖励或惩罚，从而影响驾驶员未来的行为。在作者先前的研究中，提出了一种基于发送方的信任管理框架，避免了接收方对消息的评估。然而，这并不能保证可信的驾驶员不会说谎。这些"不实攻击"通过路侧单元（RSU）协作裁决争端并给予固定奖励或惩罚来解决。本文针对这一缺乏精细度的问题，提出了一种新颖的模糊RSU控制器，该控制器综合考虑事件严重程度、驾驶员过往行为以及RSU置信度，以确定对冲突驾驶员的奖励或惩罚。尽管任何驾驶员都可能在特定情境下说谎，但可信驾驶员更可能保持诚实，反之亦然。这种特性通过马尔可夫链模型对发送方和报告方驾驶员进行刻画，其说谎特征取决于信任得分和信任状态。每个信任状态采用不同的概率分布定义驾驶员的说谎可能性。通过Veins仿真器开展广泛仿真，评估模糊评估方案的性能，并检验改变全部或部分驾驶员初始信任得分时马尔可夫链驾驶员行为模型的表现。对比固定RSU评估方案与模糊方案的结果表明，模糊方案能够有效激励驾驶员改善自身行为。