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

2025-01-04

AI Summary

GNSS/GPS systems face dual malicious interference threats—spoofing and jamming—compromising positioning, navigation, and timing (PNT) integrity. To address this, we propose the first unified detection framework that jointly models both attack types. Our approach innovatively integrates time-frequency signal features with visualized signal representations, enabling a multimodal deep learning architecture—Hybrid CNN-LSTM—that overcomes the limited generalizability and poor scene adaptability of conventional methods. Unlike prior works relying solely on signal processing or unimodal learning, our framework achieves 99% jamming detection accuracy on real-world datasets—a ~5% improvement over state-of-the-art—and sets a new benchmark for spoofing detection performance. This significantly enhances the robustness and practical deployability of PNT systems under adversarial conditions.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• GPS integrity
• Signal interference
• Positioning accuracy

Innovation

• Machine Learning
• Deep Learning
• GPS Signal Identification