A Comprehensive Forensic Framework for Unmanned Aerial Vehicle Investigations: Empirical Validation with the DJI Mavic 3 Classic

The rapid proliferation of unmanned aerial vehicles has introduced significant challenges for digital forensics, particularly due to their increasing involvement in criminal, surveillance, and security-related incidents. The heterogeneous hardware architectures, proprietary data formats, encryption mechanisms, and volatile storage characteristics of modern drones complicate reliable evidence recovery and analysis. This study proposes a comprehensive forensic framework for unmanned aerial vehicle investigations, empirically validated using the DJI Mavic 3 Classic. The proposed methodology integrates a conceptual forensic model with practical investigation procedures, including multi-source data acquisition, metadata analysis, anomaly detection, and digital twin-based reconstruction to support event correlation and timeline reconstruction. Four representative case studies: flight log recovery, firmware modification detection, metadata-driven espionage analysis, and reconstruction of deleted media are conducted to evaluate the framework’s effectiveness. Experimental results demonstrate evidence recovery rates of up to 92%, timeline reconstruction accuracy of 95%, and anti-forensic activity detection rates of 100%. The framework explicitly addresses challenges associated with proprietary formats, encryption, and data volatility in drone ecosystems. The proposed approach provides actionable guidance for drone forensics practitioners, researchers, and policymakers, contributing toward standardized and reliable forensic investigation processes for contemporary unmanned aerial vehicle platforms.

@article{Parmar2026,
  title     = {A Comprehensive Forensic Framework for Unmanned Aerial Vehicle Investigations: Empirical Validation with the DJI Mavic 3 Classic},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Nidhiba Parmar and Naveen Kumar Chaudhary},
  doi       = {10.14569/IJACSA.2026.0170113},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170113}
}