e-mail authorship verification for forensic investigation
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
The Internet provides a convenient platform for cyber criminals to anonymously conduct their illegitimate activities, such as phishing and spamming. As a result, in recent years, authorship analysis of anonymous e-mails has received some attention in the cyber forensic and data mining communities. In this paper, we study the problem of authorship verification: given a set of e-mails written by a suspect along with an e-mail dataset collected from the sample population, we want to determine whether or not an anonymous e-mail is written by the suspect. To address the problem of authorship verification of textual documents and employ detection measures that are more suited in the context of forensic investigation, we borrow the NIST's speaker recognition evaluation (SRE) framework. Our experimental results on real world e-mail dataset suggest that the employed framework addresses the e-mail authorship verification problem with a matching success as in case of speaker verification. The proposed framework produces an average equal error rate of 15--20% and minDCF equal to 0.0671 (with 10-fold cross validation technique) in correctly verifying the author of a malicious e-mail.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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