The increasing reliance on Global Positioning System (GPS) technology across various sectors has exposed vulnerabilities to malicious attacks, particularly GPS jamming and spoofing. This thesis presents an analysis into detection and mitigation strategies for enhancing the resilience of GPS receivers against jamming and spoofing attacks. The research entails the development of a simulated GPS signal and a receiver model to accurately decode and extract information from simulated GPS signals. The study implements the generation of jammed and spoofed signals to emulate potential threats faced by GPS receivers in practical settings. The core innovation lies in the integration of machine learning techniques to detect and differentiate genuine GPS signals from jammed and spoofed ones. By leveraging the machine learning capability of the Support Vector Machine (SVM) algorithm to classify signal attributes as nominal or abnormal and an Artificial Immune System (AIS) framework to create an optimized Health Management System (HMS), the system adapts and learns from various signal characteristics, enabling it to make informed decisions regarding the authenticity of the received signals. After conducting training, validation, and fault detection, the model successfully returned an average 95.3% spoofed signal detection rate. The proposed machine-learning-based detection mechanism is expected to enhance the robustness of GPS receivers against evolving spoofing techniques.
","abstract_html":"<p>The increasing reliance on Global Positioning System (GPS) technology across various sectors has exposed vulnerabilities to malicious attacks, particularly GPS jamming and spoofing. This thesis presents an analysis into detection and mitigation strategies for enhancing the resilience of GPS receivers against jamming and spoofing attacks. The research entails the development of a simulated GPS signal and a receiver model to accurately decode and extract information from simulated GPS signals. The study implements the generation of jammed and spoofed signals to emulate potential threats faced by GPS receivers in practical settings. The core innovation lies in the integration of machine learning techniques to detect and differentiate genuine GPS signals from jammed and spoofed ones. By leveraging the machine learning capability of the Support Vector Machine (SVM) algorithm to classify signal attributes as nominal or abnormal and an Artificial Immune System (AIS) framework to create an optimized Health Management System (HMS), the system adapts and learns from various signal characteristics, enabling it to make informed decisions regarding the authenticity of the received signals. After conducting training, validation, and fault detection, the model successfully returned an average 95.3% spoofed signal detection rate. The proposed machine-learning-based detection mechanism is expected to enhance the robustness of GPS receivers against evolving spoofing techniques.</p>","abstract_has_math":false,"creators":["Squatrito, Alberto"],"institution":null,"degree_name":"Master of Science in Aerospace Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-04-25T07:00:00Z","date_published":"2024-04-25T07:00:00Z","updated_at":"2026-07-27T19:25:29Z","subjects":["GPS; AI; Navigation; Machine Learning; Spoofing; Jamming; Support Vector Machine; Simulation; Artificial Immune System; Health Management System","Aerospace Engineering","Artificial Intelligence and Robotics","Programming Languages and Compilers"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/810","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Squatrito, Alberto"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Aerospace Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["GPS; AI; Navigation; Machine Learning; Spoofing; Jamming; Support Vector Machine; Simulation; Artificial Immune System; Health Management System","Aerospace Engineering","Artificial Intelligence and Robotics","Programming Languages and Compilers"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/810"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The increasing reliance on Global Positioning System (GPS) technology across various sectors has exposed vulnerabilities to malicious attacks, particularly GPS jamming and spoofing. This thesis presents an analysis into detection and mitigation strategies for enhancing the resilience of GPS receivers against jamming and spoofing attacks. The research entails the development of a simulated GPS signal and a receiver model to accurately decode and extract information from simulated GPS signals. The study implements the generation of jammed and spoofed signals to emulate potential threats faced by GPS receivers in practical settings. The core innovation lies in the integration of machine learning techniques to detect and differentiate genuine GPS signals from jammed and spoofed ones. By leveraging the machine learning capability of the Support Vector Machine (SVM) algorithm to classify signal attributes as nominal or abnormal and an Artificial Immune System (AIS) framework to create an optimized Health Management System (HMS), the system adapts and learns from various signal characteristics, enabling it to make informed decisions regarding the authenticity of the received signals. After conducting training, validation, and fault detection, the model successfully returned an average 95.3% spoofed signal detection rate. The proposed machine-learning-based detection mechanism is expected to enhance the robustness of GPS receivers against evolving spoofing techniques.
"]},{"key":"dc:title","label":"Title","values":["Machine Learning-based GPS Jamming and Spoofing Detection"]}]}],"canonical_facts":{"dc:creator":["Squatrito, Alberto"],"dc:description.abstract":["The increasing reliance on Global Positioning System (GPS) technology across various sectors has exposed vulnerabilities to malicious attacks, particularly GPS jamming and spoofing. This thesis presents an analysis into detection and mitigation strategies for enhancing the resilience of GPS receivers against jamming and spoofing attacks. The research entails the development of a simulated GPS signal and a receiver model to accurately decode and extract information from simulated GPS signals. The study implements the generation of jammed and spoofed signals to emulate potential threats faced by GPS receivers in practical settings. The core innovation lies in the integration of machine learning techniques to detect and differentiate genuine GPS signals from jammed and spoofed ones. By leveraging the machine learning capability of the Support Vector Machine (SVM) algorithm to classify signal attributes as nominal or abnormal and an Artificial Immune System (AIS) framework to create an optimized Health Management System (HMS), the system adapts and learns from various signal characteristics, enabling it to make informed decisions regarding the authenticity of the received signals. After conducting training, validation, and fault detection, the model successfully returned an average 95.3% spoofed signal detection rate. The proposed machine-learning-based detection mechanism is expected to enhance the robustness of GPS receivers against evolving spoofing techniques.
"],"dc:identifier":["https://commons.erau.edu/edt/810"],"dc:subject":["GPS; AI; Navigation; Machine Learning; Spoofing; Jamming; Support Vector Machine; Simulation; Artificial Immune System; Health Management System","Aerospace Engineering","Artificial Intelligence and Robotics","Programming Languages and Compilers"],"dc:title":["Machine Learning-based GPS Jamming and Spoofing Detection"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Aerospace Engineering"]},"updated_at":"2026-07-27T19:25:29Z"}