Smart Verification of Unmanned Aerial Vehicle GPS Geolocation via Received Signal Strength Indicators

Arupa Sarkar, Fendy Santoso, Akbar Telikani, Jun Shen, Bo Du, Jun Yan

Abstract: The increased reliance on Unmanned Aerial Vehicles (UAVs) in various industries exalts the security requirements since it is critical to protect these systems from any cyber-attack. GPS spoofing presents an important challenge by deceiving UAVs through false GPS signals that would disrupt their operations, thereby endangering them. As a countermeasure, this study introduces a method of detecting GPS spoofing attacks that are aimed at UAV systems. This involves developing a robust methodology to detect the GPS spoofing attack based on the UAV’s current reported location and Received Signal Strength (RSS) data at several base stations. In this study, we developed a smart verification algorithm using the K-Nearest Neighbors (KNN) algorithm to authenticate the reported locations of UAVs, based on RSS from various base stations antenna. We evaluated the performance of the algorithm using metrics such as accuracy, precision, and F1-score. The results indicate that the algorithm’s effectiveness improves with an increase in the number of base stations used. Additionally, the paper will pinpoint the possible direction for UAV security and the adaptive countermeasures to improve the level of resilience against spoofing tactics, which are rapidly evolving.