Database Systems Examination and Digital Forensics Tool: The Progress and Limitations
Bibliographic record
Abstract
Databases play a critical role in many computing systems and applications and provide an excellent source of information for forensics investigations. Also, with many advances in the digital forensics field, several forensic tools can be employed for different purposes in digital investigations. Despite this, many forensics tools have limitations when it pertains to the collection, examination, or analysis of potential evidence from database management systems due to the inherent complexities of handling different database systems. This is particularly true for free or open-source tools that can be used for research purposes, and this limits the development of new tools and solutions for database forensics. To address this and forge a path for the development of new tools for database forensics, this paper highlights some of the available tools that can be used for database forensics, the limitations of using some of these tools, and the challenges of performing database analysis in general. We achieve this through a practical analysis of the tools and examine their capabilities in terms of supporting the forensics analysis of databases found on digital devices. Given their popularity, we consider databases found on both Android and iPhone devices, as well as other data sources. We integrated an analysis of relevant testing reports from the Computer Forensics Tool Testing (CFTT) program to provide a complete picture of the forensic tools. This paper establishes the aspects where these tools can be improved and provides recommendations for handling some of the challenges associated with the forensics analysis of database systems.
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.
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 | 0.040 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".