Year: 2025
Type: conference-paper
Abstract: Global Navigation Satellite Systems (GNSS) have become an integral part of the modern era, to deliver essential positioning, navigation, and timing (PNT) services for numerous applications. However, the increasing reliance on GNSS has also made these systems vulnerable to various security threats, particularly jamming and spoofing attacks. This comprehensive review examines the state-of-the-art in GNSS spoofing and jamming mitigation techniques, with a special focus on machine learning approaches. Based on an analysis of the 30 papers from IEEE, the Institute of Navigation (ION), and Q1 journals, this review categorizes and evaluates different mitigation strategies, compares their effectiveness against various attack types, and identifies emerging trends and future research directions. The paper includes detailed tables, graphs, and visualizations to facilitate understanding of the complex landscape of GNSS security. The findings indicate that while traditional signal processing techniques remain valuable, machine learning approaches are increasingly demonstrating superior performance in detecting and mitigating sophisticated attacks, suggesting a promising direction for future research and development in GNSS security.
Authors: Adel Noah, Mohammed Elmusrati
Institutions: University of Benghazi, University of Vaasa
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414464923