A Computational Model for Reputation and Ensemble-Based Learning Model for Prediction of Trustworthiness in Vehicular Ad Hoc Network
A Computational Model for Reputation and Ensemble-Based Learning Model for Prediction of Trustworthiness in Vehicular Ad Hoc Network
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DOI：
10.1109/jiot.2023.3279950
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发表时间：
2023-10
影响因子：
10.6
通讯作者：
Abdullah Alharthi;Q. Ni;Richard Jiang;Mohammad Ayoub Khan
中科院分区：
文献类型：
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作者：
Abdullah Alharthi;Q. Ni;Richard Jiang;Mohammad Ayoub Khan
车辆自组织网络（vanet）是一种特殊的无线通信网络，它促进了车对车（V2V）和车对基础设施（V2I）的通信。这项技术显示出提高道路安全性、交通效率和乘客舒适度的潜力。然而，这可能会导致潜在的安全隐患和安全风险，特别是在严重依赖与其他车辆和基础设施通信的自动驾驶汽车中。信任、数据的准确性和通过通信信道传输的数据的可靠性是VANET的主要问题。基于密码学的解决方案在确保数据传输的安全性方面已经取得了成功。然而，仍然需要进一步的研究来解决从合法发送者发送的欺诈性信息的问题。因此，在本研究中，我们提出了一种计算车辆声誉并随后预测网络中车辆可信度的方法。区块链记录了对车辆可信度的最新评估。这将提高车辆历史的透明度和信任度，并降低欺诈或篡改信息的风险。车辆的可信度不仅取决于其可信度，还取决于其在数据传输过程中观察到的网络行为。为了对信任进行分类，使用了集成学习模型。在数据集上运行深度测试，以评估所提出的集成学习与特征选择技术的有效性。研究结果表明，所提出的集成学习技术准确率达到99.98%，明显优于基线模型的准确率。
Vehicular ad hoc networks (VANETs) are a special kind of wireless communication network that facilitates vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. This technology exhibits the potential to enhance the safety of roads, efficiency of traffic, and comfort of passengers. However, this can lead to potential safety hazards and security risks, especially in autonomous vehicles that rely heavily on communication with other vehicles and infrastructure. Trust, the precision of data, and the reliability of data transmitted through the communication channel are the major problems in VANET. Cryptography-based solutions have been successful in ensuring the security of data transmission. However, there is still a need for further research to address the issue of fraudulent messages being sent from a legitimate sender. As a result, in this study, we have proposed a methodology for computing vehicle’s reputation and subsequently predicting the trustworthiness of vehicles in networks. The blockchain records the most recent assessment of the vehicle’s credibility. This will allow for greater transparency and trust in the vehicle’s history, as well as reduce the risk of fraud or tampering with the information. The trustworthiness of a vehicle is confirmed not just by the credibility, but also by its network behavior as observed during data transfer. To classify the trust, an ensemble learning model is used. In depth tests are run on the data set to assess the effectiveness of the proposed ensemble learning with feature selection technique. The findings show that the proposed ensemble learning technique achieves a 99.98% accuracy rate, which is notably superior to the accuracy rates of the baseline models.