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. 

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）。