While Vehicular Ad-hoc Network (VANET) is developed to enable effective vehicle communication and traffic information exchange, VANET is also vulnerable to different security attacks, such as DOS attacks. The usage of an intrusion detection system (IDS) is one possible solution for preventing attacks in VANET. However, dealing with a large amount of vehicular data that keep growing in the urban environment is still an critical challenge for IDSs. This paper, therefore, proposes a new machine learning model to improve the performance of IDSs by using Random Forest and a posterior detection based on coresets to improve the detection accuracy and increase detection efficiency. The experimental results show that the proposed machine learning model can significantly enhance the detection accuracy compared to classical application of machine learning models.

虽然车辆自组织网络 (VANET) 的开发是为了实现有效的车辆通信和交通信息交换，但 VANET 也容易受到不同的安全攻击，例如 DOS 攻击。使用入侵检测系统 (IDS) 是防止 VANET 攻击的一种可能解决方案。然而，处理在城市环境中不断增长的大量车辆数据仍然是 IDS 面临的关键挑战。因此，本文提出了一种新的机器学习模型，通过使用随机森林和基于核心集的后验检测来提高 IDS 的性能，以提高检测精度并提高检测效率。