A survey on forensic event reconstruction systems
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
Security related incidents such as unauthorised system access, data tampering and theft have been noticeably rising. Tools such as firewalls, intrusion detection systems and anti-virus software strive to prevent these incidents. Since these tools only prevent an attack, once an illegal intrusion occurs, they cease to provide useful information beyond this point. Consequently, system administrators are interested in identifying the vulnerability in order to: 1) avoid future exploitation; 2) recover corrupted data; 3) present the attacker to law enforcement where possible. As such, forensic event reconstruction systems are used to provide the administrators with possible information. We present a survey on the current approaches towards forensic event reconstruction systems proposed over the past few years. Technical details are discussed, as well as analysis to their effectiveness, advantages and limitations. The presented tools are compared and assessed based on the primary principles that a forensic technique is expected to follow.
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 | 0.000 | 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 | 0.002 | 0.004 |
| Open science | 0.001 | 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