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.