A Comprehensive Cybersecurity Framework for UAV Systems: Multi-Vector Threat Analysis and Defense Evaluation

https://doi.org/10.5281/zenodo.17897974

Abstract

The increasing integration of unmanned aerial vehicles (UAVs) into critical sectors such as energy, defense, transportation, and public safety has introduced significant cybersecurity challenges. UAVs rely on wireless communication links, GPS navigation, onboard sensors, and software-controlled payloads, creating multiple attack surfaces that adversaries can exploit for disruption or unauthorized access. This study presents a comprehensive analysis of UAV cybersecurity by examining real-world incidents, modeling multi-vector attack scenarios, and evaluating modern defense mechanisms. Results from 50 documented cases and simulation experiments reveal that GPS spoofing is the most successful attack vector, achieving a 78% success rate due to the reliance on unencrypted navigation signals. AI-based anomaly detection demonstrates the highest detection accuracy (91%) and mitigation efficiency (88%), outperforming radar, RF scanning, and geofencing. Sector-specific analysis shows that the energy and defense sectors are the most vulnerable to UAV intrusions, with impact scores of 9.1 and 8.7 respectively. The findings highlight critical gaps in current defense systems and show that isolated measures such as geofencing or RF blocking are insufficient against sophisticated multi-vector threats. This study recommends a layered defense approach that combines AI-driven monitoring, secure communication protocols, environmental controls, and regulatory enforcement to enhance UAV cybersecurity and protect critical infrastructure from emerging aerial threats.