A DATASET for GPS Spoofing Detection on Autonomous Vehicles

A dataset of Global Positioning System (GPS) spoofing attacks is presented in this article. This dataset includes data extracted from authentic GPS signals collected from different locationsto emulate a moving and a static autonomous vehicle using a universal software radio peripheral unit configured as a GPS receiver. During the data collection, 13 features are extracted from eight-parallel channels at different receiver stages (i.e., acquisition, tracking, and navigation decoding). In addition to the collected authentic GPS signals, three GPS spoofing attack types were simulated, simplistic, intermediate, and sophisticated attacks. The resultant dataset contains a total of 158,170 samples, including 55% of legitimate instances and 45% of samples corresponding to three types of simulated GPS spoofing attacks, all in a balanced distribution. The data described and attached to this article can be used to investigate the effect of the GPS spoofing attack on the extracted features and contribute to the development of GPS spoofing attack detection techniques based on supervised and unsupervised machine learning. 

本文提出了一套全球定位系统（Global Positioning System，GPS）欺骗攻击数据集。该数据集包含从不同地点采集的真实GPS信号中提取的数据，通过配置为GPS接收机的通用软件无线电外设（universal software radio peripheral，USRP）单元来模拟移动与静止两种状态的自动驾驶车辆。在数据采集阶段，研究人员从接收机不同工作阶段（即捕获、跟踪与导航解码阶段）的8个并行信道中提取了13项特征。除采集到的真实GPS信号外，本次研究还模拟了三类GPS欺骗攻击：简单型、中间型与复杂型攻击。最终生成的数据集共包含158170条样本，其中合法样本占比55%，对应三类模拟GPS欺骗攻击的样本占比45%，整体分布均衡。本文所描述并附带的数据集可用于研究GPS欺骗攻击对所提取特征的影响，同时可为基于监督与无监督机器学习的GPS欺骗攻击检测技术的研发提供支撑。

搜集汇总

背景与挑战

背景概述

该数据集包含来自真实GPS信号和三种模拟欺骗攻击（简单、中级和复杂）的158,170个样本，用于研究GPS欺骗攻击对自动驾驶车辆的影响。数据集特征包括13个从八个并行通道提取的参数，覆盖接收器的不同阶段，且正负样本分布均衡（55%合法样本和45%攻击样本）。