A Formal Approach for the Forensic Analysis of Logs
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 increasing trend of computer crimes has intensified the relevance of cyber-forensics. In such a context, forensic analysis plays a major role by analyzing the evidence gathered from the crime scene and corroborating facts about the committed crime. In this paper, we propose a formal approach for the forensic log analysis. The proposed approached is based on the logical modelling of the events and the traces of the victim system as formulas over a modified version of the ADM logic[12]. In order to illustrate the proposed approach, the Windows auditing system[21] is studied. We will discuss the importance of the different features of such a system from the forensic standpoint (e.g. the ability to log accesses to specific files and registry keys and the abundance of information that can be extracted from these logs). Furthermore, we will capture logically: Invariant properties of a system, forensic hypotheses, generic or specific attack signatures. Moreover, we will discuss the admissibility of forensics hypotheses and the underlying verification issues.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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