Cyber Forensics: Representing and (Im)Proving the Chain of Custody Using the Semantic web
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract- Computer/Digital forensic is still in its infancy, but it is a very growing field. It involves extracting evidences from digital device in order to analyze and present them in a court of law to prosecute it. Digital evidences can be easily altered if proper precautions are not taken. A chain of custody (CoC) document is used to demonstrate the road map of how evidences have been copied, transported, and stored throughout the investigation process. With the advent of the digital age, the tangible CoC document needs to undergone a radical transformation from paper to electronic data (e-CoC), readable and consumed by machines, and applications. Semantic web is a flexible solution to represent different information, because it provides semantic markup languages for knowledge representation, supported by different vocabularies for provenance information. These features can be exploited to represent the tangible COC document to ensure its trustworthiness and its integrity. Moreover, querying mechanisms can be also incorporated over this represented knowledge to answer different forensic and provenance questions asked by juries concerning the case in hand. Thus, this paper proposes the construction of a framework solution based on the semantic web to represent and consume the forensic and provenance knowledge related to the tangible COC document.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it