Mekonen Bekan Kitaw, Bane Lemi, Fite Negasa Berhanu
Faculty of Computing and Informatics, Jimma Institute of Technology, Jimma University, Jimma, Oromia, Ethiopia.
Department of Computer Science, Dambi Dollo University, Dembi Dolo, Oromia, Ethiopia.
PLoS One. 2025 Aug 1;20(8):e0328829. doi: 10.1371/journal.pone.0328829. eCollection 2025.
Vehicular Ad-hoc Networks (VANETs) are critical to Intelligent Transportation Systems (ITS), enabling vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication to improve road safety and traffic flow. However, VANETs face significant security threats, particularly position falsification attacks, where malicious nodes disseminate false Basic Safety Messages (BSMs). This study proposes an ensemble learning framework to detect such attacks, leveraging Decision Tree (CART), Random Forest, K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP) classifiers enhanced with bagging. Using the VeReMi dataset, our RSU-level detection system analyzes sequential BSMs to detect malicious behavior. Results demonstrate that KNN with bagging achieves perfect precision, recall, accuracy, and F1 score (100%) for Attack 1, while maintaining near-perfect performance for complex attacks like Attack 2 (99.87% accuracy) and Attack 16 (97.85% accuracy). Decision Tree with bagging also performs well for simpler attacks but experiences a slight decline for highly complex scenarios. Random Forest with bagging excels in simpler attacks but struggles with complex patterns. MLP with bagging shows strong results for simpler attacks but underperforms in complex scenarios. The proposed framework highlights the effectiveness of ensemble techniques, particularly KNN with bagging, in safeguarding VANET communication systems, offering a scalable, efficient, and robust solution for VANET security.
车载自组织网络（VANETs）对智能交通系统（ITS）至关重要，它能实现车对车（V2V）和车对基础设施（V2I）通信，以提高道路安全和交通流量。然而，VANETs面临重大安全威胁，尤其是位置伪造攻击，恶意节点会传播虚假的基本安全消息（BSM）。本研究提出了一个集成学习框架来检测此类攻击，利用决策树（CART）、随机森林、K近邻（KNN）和通过装袋增强的多层感知器（MLP）分类器。使用VeReMi数据集，我们的路边单元（RSU）级检测系统分析连续的BSM以检测恶意行为。结果表明，装袋的KNN在攻击1中实现了完美的精确率、召回率、准确率和F1分数（100%），而在攻击2（准确率99.87%）和攻击16（准确率97.85%）等复杂攻击中保持近乎完美的性能。装袋的决策树在较简单的攻击中也表现良好，但在高度复杂的场景中略有下降。装袋的随机森林在较简单的攻击中表现出色，但在处理复杂模式时存在困难。装袋的MLP在较简单的攻击中显示出强劲的结果，但在复杂场景中表现不佳。所提出的框架突出了集成技术的有效性，特别是装袋的KNN，在保障VANET通信系统安全方面，为VANET安全提供了一种可扩展、高效且强大的解决方案。
