An Assessment of, and Improvements to, the Digital Forensics Acquisition Process of a Law Enforcement Agency
Bibliographic record
Abstract
Forensics addresses the collection and analysis of evidence. Digital forensics is forensics in the context of digital devices. It is a rapidly evolving field employed in various organizations such as law enforcement, government, and the private sector. The acquisition of digital evidence is the step in digital forensics where digital evidence is preserved. The preservation of digital evidence in its original form is customarily deemed a necessary property in the context of digital forensics, as such evidence may need to be re-examined in the future. \n \nIn this thesis, we first analyze the acquisition phase of the digital forensics process of the Ontario Provincial Police (OPP) to determine whether it is forensically sound. The OPP is a law enforcement agency that serves a population of 14 million people who reside in the province of Ontario in Canada. We extract a set of properties that OPP's acquisition phase does, and should, uphold to achieve forensic soundness. We then evaluate whether the desired properties are met by comparing OPP's process to three standards on forensic soundness for law enforcement. We conclude by proposing improvements to the parts of the process that do not uphold desired properties. \n \nWhile our thesis evaluates and provides suggestions to OPP's current process, it also serves a greater purpose. Our contributions allow OPP, and any other law enforcement agency, the framework needed to analyze an existing process, identify areas that may jeopardize forensic soundness, and implement changes that mitigate those threats.
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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 | 0.036 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| 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".