GNSS/GPS Spoofing and Jamming Identification Using Machine Learning and Deep Learning

TL;DR

This paper explores the use of machine learning and deep learning techniques to detect GNSS/GPS spoofing and jamming attacks, achieving high accuracy and demonstrating the effectiveness of these methods in real-world scenarios.

Contribution

It introduces novel machine learning and deep learning approaches for GNSS/GPS spoofing and jamming detection, with extensive experiments on real datasets showing state-of-the-art performance.

Findings

Achieved approximately 99% accuracy in jamming detection.

Improved detection performance by around 5% over previous methods.

Validated effectiveness of advanced algorithms on real-world datasets.

Abstract

The increasing reliance on Global Navigation Satellite Systems (GNSS), particularly the Global Positioning System (GPS), underscores the urgent need to safeguard these technologies against malicious threats such as spoofing and jamming. As the backbone for positioning, navigation, and timing (PNT) across various applications including transportation, telecommunications, and emergency services GNSS is vulnerable to deliberate interference that poses significant risks. Spoofing attacks, which involve transmitting counterfeit GNSS signals to mislead receivers into calculating incorrect positions, can result in serious consequences, from navigational errors in civilian aviation to security breaches in military operations. Furthermore, the lack of inherent security measures within GNSS systems makes them attractive targets for adversaries. While GNSS/GPS jamming and spoofing systems consist of numerous components, the ability to distinguish authentic signals from malicious ones is essential for maintaining system integrity. Recent advancements in machine learning and deep learning provide promising avenues for enhancing detection and mitigation strategies against these threats. This paper addresses both spoofing and jamming by tackling real-world challenges through machine learning, deep learning, and computer vision techniques. Through extensive experiments on two real-world datasets related to spoofing and jamming detection using advanced algorithms, we achieved state of the art results. In the GNSS/GPS jamming detection task, we attained approximately 99% accuracy, improving performance by around 5% compared to previous studies. Additionally, we addressed a challenging tasks related to spoofing detection, yielding results that underscore the potential of machine learning and deep learning in this domain.