Live Forensic Environment with Parallel Data Acquisition for Investigating Private Mode Browsing
Bibliographic record
Abstract
Web Browser Private mode is a feature hide activities carried out by users, user activities not stored on the hard disk or SSD, so they cannot be analyzed with standard forensic techniques (static forensics). This study aimed to acquire web browser artefacts from Random Access Memory (RAM) and Network Traffic on Windows 10 and Ubuntu Linux operating systems. The problem of this study is how to find evidence of a crime in a web browser that utilizes the private mode feature. The web browser in private mode does not leave any traces on the User's computer, so it becomes an obstacle in the process of searching for digital evidence of a crime. This article proposed parallel data acquisition data through two different sources to obtain digital evidence using private mode web browsers, from RAM and Network Traffic. All website log on Windows 10 and Ubuntu 20.04 are founded with RAM analysis. Analysis network traffic also revealed (1) source IP address, (2) destination IP address, (3) protocol, (4) source port, (5) destination port, (6) timestamp (communication time done), (
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".