TY  - EJOU
AU  - Murtaza, Ahmed 
AU  - Memon, Abdullah 
AU  - Hafeez, Sana 
AU  - Ali, Muzammil 
AU  - Abro, Ghulam E Mustafa 

TI  - Cybersecurity Threat Modeling and Privacy Risk Assessment in Collaborative Civilian UAV Systems
T2  - Intelligent Automation \& Soft Computing

PY  - 2026
VL  - 41
IS  - 1
SN  - 2326-005X

AB  - Civilian Unmanned Aerial Systems (UAS) are increasingly deployed in smart-city monitoring, infrastructure inspection, logistics, and emergency response applications. However, their integration with wireless networks, cloud services, and AI-driven analytics significantly expands cybersecurity and privacy risks. Existing studies mainly focus on isolated technical vulnerabilities such as GNSS spoofing, jamming, and communication attacks, while lacking a unified framework that systematically connects cyber threats with quantitative privacy risk assessment. To address this research gap, this study proposes a layered threat-modeling framework for collaborative civilian UAS based on multidimensional attack-surface analysis and STRIDE-oriented threat mapping. In addition, a quantitative privacy risk model is developed by integrating compromise likelihood, data sensitivity, contextual amplification, downstream misuse potential, and regulatory exposure. The proposed framework is evaluated through three representative deployment scenarios: smart-city surveillance, critical infrastructure monitoring, and mega-event security operations. The findings demonstrate that technical compromises in UAS ecosystems can propagate into large-scale societal and privacy harms, particularly in highly connected urban environments. The study contributes a structured cybersecurity and privacy assessment methodology, a UAS-specific privacy risk quantification model, and engineering as well as governance-oriented mitigation strategies to support resilient, privacy-aware, and regulation-compliant civilian UAS deployment.
KW  - Unmanned aerial systems; cybersecurity; privacy risk; threat modeling; civilian UAV; attack surface; risk quantification; resilience engineering

DO  - 10.32604/iasc.2026.082765