Extraction of Common Processes of the Investigation Models Proposed in the Drone Forensics Domain
Bibliographic record
Abstract
A recent development in the field of digital forensics is drone forensic (DF), which involves collecting evidence from drone environments.Nevertheless, DF still suffers from a number of issues and challenges that have recently been discovered.The complexity of DF infrastructures is still a key issue that needs to be resolved.Furthermore, redundancy challenges are important obstacles and constraints in DF investigations.The present study proposes a model called Unified Investigation Processes for Drone Forensics Domain (UIP-DFD) in order to identify the investigation processes commonly involved in the models proposed in the DF domain.Furthermore, this study used the design science research (DSR) approach to design an effective and efficient method for analyzing unmanned aerial vehicle (UAV) evidence, ensuring the evidence is identified, gathered, and analyzed based on recognized DF investigation techniques.UIP-DFD comprises five common investigation processes: i) Identification, ii) Data acquisition, iii) Preservation, iv) Data analysis, and v) Reporting.After conducting a comparative analysis, this study concludes that the NIST digital forensic framework is inadequate for DF.In contrast, the proposed UIP-DFD model integrates drone-specific investigation activities, minimizing redundancy and effectively managing the diversity of evidence from onboard systems, controllers, storage devices, and other digital sources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".