Privacy Preserving Misbehavior Detection in IoV Using Federated Machine Learning
Privacy Preserving Misbehavior Detection in IoV Using Federated Machine Learning
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DOI：
10.1109/ccnc49032.2021.9369513
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Aashma Uprety;D. Rawat;Jiang Li
中科院分区：
文献类型：
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作者：
Aashma Uprety;D. Rawat;Jiang Li
针对车联网(IoV)的车载自组织网络(VANET)中的数据篡改攻击是通过破坏含有虚假信息的节点之间交换的数据来实现的。如今，数据是最有价值的资产，可以从中得出许多分析和结果。但用户提出的隐私担忧成为进行数据分析的最大障碍。在IoV中，可以通过从车辆的基本安全消息(BSM)数据集创建机器学习模型来执行不当行为检测。提出了一种基于联邦机器学习的IoV隐私保护不当行为检测系统。针对IoV的VANET中的车辆被赋予使用自己的本地数据进行本地训练的初始Dull模型。在此基础上，我们得到了一个集合的智能模型，该模型可以利用每辆车产生的数据对车载自组网中的位置篡改攻击进行分类。所有这些都是在没有实际与任何第三方共享数据以执行分析的情况下完成的。在本文中，我们比较了使用联邦方法和中心方法训练的攻击检测模型的性能。这种训练方法通过使用每辆车上生成的局部BSM数据来训练模型对不同类型的位置篡改攻击。
Data falsification attack in Vehicular Ad hoc Networks (VANET) for the Internet of Vehicles (IoV) is achieved by corrupting the data exchanged between nodes with false information. Data is the most valuable asset these days from which many analyses and results can be drawn out. But the privacy concern raised by users has become the greatest hindrance in performing data analysis. In IoV, misbehavior detection can be performed by creating a machine learning model from basic safety message (BSM) dataset of vehicles. We propose a privacy-preserving misbehavior detecting system for IoV using Federated Machine Learning. Vehicles in VANET for IoV are given the initial dull model to locally train using their own local data. On doing this we get a collective smart model that can classify Position Falsification attack in VANET using the data generated by each vehicle. All this is done without actually sharing the data with any third party to perform analysis. In this paper, we compare the performance of the attack detection model trained by using a federated and central approach. This training method trains the model on a different kind of position falsification attack by using local BSM data generated on each vehicle.