Hassan Fayaz, Yu Jianguo, Syed Zafi Sherhan, Ahmed Nadeem, Reshan Mana Saleh Al, Shaikh Asadullah
Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
Department of Telecommunication Engineering, Mehran University of Engineering and Technology Jamshoro, Jamshoro, Pakistan.
PeerJ Comput Sci. 2023 Jun 22;9:e1440. doi: 10.7717/peerj-cs.1440. eCollection 2023.
Vehicular networks (VANETs) are intelligent transport subsystems; vehicles can communicate through a wireless medium in this system. There are many applications of VANETs such as traffic safety and preventing the accident of vehicles. Many attacks affect VANETs communication such as denial of service (DoS) and distributed denial of service (DDoS). In the past few years the number of DoS (denial of service) attacks are increasing, so network security and protection of the communication systems are challenging topics; intrusion detection systems need to be improved to identify these attacks effectively and efficiently. Many researchers are currently interested in enhancing the security of VANETs. Based on intrusion detection systems (IDS), machine learning (ML) techniques were employed to develop high-security capabilities. A massive dataset containing application layer network traffic is deployed for this purpose. Interpretability technique Local interpretable model-agnostic explanations (LIME) technique for better interpretation model functionality and accuracy. Experimental results demonstrate that utilizing a random forest (RF) classifier achieves 100% accuracy, demonstrating its capability to identify intrusion-based threats in a VANET setting. In addition, LIME is applied to the RF machine learning model to explain and interpret the classification, and the performance of machine learning models is evaluated in terms of accuracy, recall, and F1 score.
车载网络（VANETs）是智能交通子系统；在该系统中车辆可通过无线介质进行通信。VANETs有许多应用，如交通安全和预防车辆事故。许多攻击会影响VANETs通信，如拒绝服务（DoS）和分布式拒绝服务（DDoS）。在过去几年中，DoS（拒绝服务）攻击的数量不断增加，因此网络安全和通信系统保护是具有挑战性的课题；入侵检测系统需要改进以有效且高效地识别这些攻击。目前许多研究人员对增强VANETs的安全性感兴趣。基于入侵检测系统（IDS），采用机器学习（ML）技术来开发高安全能力。为此部署了一个包含应用层网络流量的海量数据集。采用可解释性技术局部可解释模型无关解释（LIME）技术来更好地解释模型功能和准确性。实验结果表明，使用随机森林（RF）分类器可实现100%的准确率，证明其在VANET环境中识别基于入侵的威胁的能力。此外，将LIME应用于RF机器学习模型以解释分类，并根据准确率、召回率和F1分数评估机器学习模型的性能。
