A Formal Approach for the Forensic Analysis of Logs
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.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".