VANET Cybersecurity Attack Simulation Dataset

本数据集由牛津布鲁克斯大学的研究团队创建，专注于模拟和分析VANET中的网络安全攻击。数据集通过Eclipse MOSAIC模拟框架生成，涵盖了重放攻击和虚假信息攻击等多种攻击场景。该数据集旨在为研究人员提供一个平台，以训练和评估针对这些攻击的机器学习模型，从而增强自动驾驶车辆的安全性。数据集内容包括车辆状态、通信消息和攻击模拟结果，适用于研究和开发网络安全解决方案。

This dataset was developed by a research team from Oxford Brookes University, focusing on simulating and analyzing cybersecurity attacks in Vehicular Ad Hoc Networks (VANETs). Generated via the Eclipse MOSAIC simulation framework, the dataset encompasses multiple attack scenarios such as replay attacks and false information attacks. It aims to provide researchers with a platform to train and evaluate machine learning models targeted at these attacks, so as to bolster the safety of autonomous vehicles. The dataset comprises vehicle status data, communication messages, and attack simulation outcomes, and is applicable to the research and development of cybersecurity solutions.

搜集汇总

背景与挑战

背景概述

该数据集由牛津布鲁克斯大学研究团队创建，通过Eclipse MOSAIC模拟框架生成，专注于模拟VANET中的网络安全攻击，如重放攻击和虚假信息攻击。数据集内容包括车辆状态、通信消息和攻击模拟结果，旨在为研究人员提供平台，以训练和评估机器学习模型，从而提升自动驾驶车辆的安全性。