Mobile Forensic Analysis of Signal Messenger Application on Android using Digital Forensic Research Workshop (DFRWS) Framework
Bibliographic record
Abstract
Cybercrime is a crime committed using equipment connected to the internet. One of the cybercrimes that occured during the COVID-19 pandemic was the spread hoaxes about the COVID-19 vaccine which caused panic in society. Signal Messenger is one of the social media that has become a trending topic since the number of personal data security issues and the emergence of end-to-end encryption features. This research aims to find digital evidence on Signal Messenger application installed on the perpetrator's Android smartphone. This research uses Belkasoft, Magnet AXIOM, and MOBILedit Forensic Express tools and implements the Digital Forensics Research Workshop (DFRWS) framework in each stage of the research experiment. The research was carried out according to the case scenario with 11 predetermined parameters. Digital evidence is found from the Signal Messenger application: application information, account information, chat, pictures, videos, contacts, and stickers. The results of this research indicate that Belkasoft Evidence Center forensic tool is better, with an accuracy rate of 78.69%, while Magnet AXIOM is 26.23% and MOBILedit Forensic Express is 9.84%. The results of this research can be used as a reference for other forensic researchers/experts in handling similar crime cases on the Signal Messenger application to get better results.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".