With the rapid growth of Vehicle Ad-hoc Network (VANET) as a promising technology for efficient and reliable communication among vehicles and infrastructure, the security and integrity of VANET communications has become a critical concern. One of the significant threats to VANET is the presence of blackhole attacks, where malicious nodes disrupt the network's functionality and compromise data confidentiality, integrity, and availability. In this paper, we propose a machine learning-based approach for blackhole detection in VANET. To achieve this task, we first create a comprehensive dataset comprising normal and malicious traffic flows. Afterward, we study and define a promising set of features to discriminate the blackhole attacks. Finally, we evaluate various machine learning algorithms, including Gradient Boosting, Random Forest, Support Vector Machines, k-Nearest Neighbors, Gaussian Naive Bayes, and Logistic Regression. Experimental results demonstrate the effectiveness of these algorithms in distinguishing between normal and malicious nodes. Our findings also highlight the potential of machine learning based approach in enhancing the security of VANET by detecting and mitigating blackhole attacks.

翻译：随着车载自组织网络(VANET)作为实现车辆与基础设施间高效可靠通信的前沿技术迅猛发展，VANET通信的安全性与完整性已成为关键问题。VANET面临的主要威胁之一是黑洞攻击——恶意节点会破坏网络功能，危及数据的机密性、完整性和可用性。本文提出一种基于机器学习的VANET黑洞攻击检测方法。为实现该目标，我们首先构建了包含正常与恶意流量流的综合数据集，继而研究并定义了一组可有效区分黑洞攻击的特征集，最后评估了多种机器学习算法，包括梯度提升、随机森林、支持向量机、k近邻、高斯朴素贝叶斯及逻辑回归。实验结果表明，这些算法能有效区分正常节点与恶意节点。研究结果还凸显了基于机器学习的方法通过检测与缓解黑洞攻击来增强VANET安全性的潜力。