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在VANET中通过包装集体学习检测虚假位置攻击
Bekan Kitaw Mekonen1, Lemi Bane2, Negasa Berhanu Fite1
1Faculty of Computing and Informatics, Jimma Institute of Technology, Jimma University, Jimma, Oromia, Ethiopia.
PloS one
|August 1, 2025
概括
本研究介绍了一个集体学习框架,用于检测车载临时网络 (VANET) 中的位置伪造攻击. 使用袋装的K-Nearest Neighbors (KNN) 实现了完美的检测率,提高了智能交通系统的道路安全.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 智能运输系统 (ITS) 是一种智能运输系统.
背景情况:
- 车辆特设网络 (VANET) 对于ITS中的车辆到车辆 (V2V) 和车辆到基础设施 (V2I) 通信至关重要.
- 范特易受安全威胁的影响,特别是使用虚假的基本安全信息 (BSM) 来进行位置伪造攻击.
研究的目的:
- 提出一个集体学习框架,用于检测VANET中的位置伪造攻击.
- 评估各种分类器的性能,包括决策树 (CART),随机森林,K-最近邻居 (KNN) 和多层感知器 (MLP),通过袋装增强.
主要方法:
- 使用VeReMi数据集进行分析.
- 实施了路边单位 (RSU) 级探测系统,分析了连续的BSM.
- 采用集体学习技术,使用袋装来增强分类器.
主要成果:
- 使用袋装的KNN实现了100%的精度,回忆,准确性和F1得分,用于攻击1.
- 装袋的KNN在复杂的攻击中表现出近乎完美的性能 (攻击2: 99.87%的准确性,攻击16:97.85%的准确性).
- 其他组合方法的有效性各不相同,袋装KNN是最强大的.
结论:
- 集体学习技术,特别是带袋式KNN,对于检测VANET中复杂的攻击非常有效.
- 拟议的框架为保护VANET通信提供了一个可扩展,高效和强大的解决方案.
- 这种方法显著提高了智能运输系统的安全性.
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