Multiclass Classification for Detection of GPS Spoofing and Jamming Attacks on UAVs

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly being employed across various domains, making them more vulnerable to a range of attacks, particularly cyber threats. These vehicles usually rely on a global navigation satellite system (GNSS), such as the Global Positioning System (GPS) satellites, for location and navigation data, which can be exploited by adversaries launching attacks using fake GPS signals. To safeguard UAVs from GPS Jamming and GPS Spoofing attacks, this paper proposes an Intrusion Detection System (IDS) that utilizes machine learning techniques for detecting and identifying such attacks. The IDS analyzes GPS signal samples representing normal operation, GPS Jamming, and three types of GPS Spoofing attacks. It relies on machine learning, with models trained and tested for binary class and multiclass classification. The binary class version aims to identify an occurrence of any attack, irrespective of type, as suggested by previous literature. However, the novelty of this work lies in the multiclass version, which enables the identification of attack types — an essential factor in determining the most effective protective measures and providing data for forensic investigations. Stacking, an ensemble machine learning method, yielded the best results, achieving an accuracy rate of 96.91%. Furthermore, the proposed multiclass IDS reduced false negatives to 0.71%, leading to an improved IDS that reduces the likelihood of overlooking attacks compared to the binary class version, which is crucial in real UAV deployments.