Toward Formal Reasoning in Cyberforensic Case Investigation with Forensic Lucid
Bibliographic record
Abstract
This work focuses on the application of the intensional logic to cyberforensic analysis and its benefits and difficulties are compared with the finite-state automata approach. This work extends the use of the scientific intensional programming paradigm onto modeling and implementation of a cyberforensics investigation process with the backtrace of event reconstruction, modeling the evidence as multidimensional hierarchical contexts, and proving or disproving the claims with it in the intensional manner of evaluation. This is a practical, context-aware improvement over the finite state automata (FSA) approach we have seen in the related works. As a base implementation language model we use in this approach is a new dialect of the Lucid programming language, that we call Forensic Lucid and we define hierarchical contexts based on the intensional logic for the evaluation of cyberforensic expressions. We also augment the work with the credibility factors surrounding digital evidence and witness accounts, which have not been previously modeled. The Forensic Lucid programming language proposed for this intensional cyberforensic analysis, includes the syntax and operational semantics. In large part, the language is based on its predecessor and codecessor Lucid dialects, such as GIPL, Indexical Lucid, Lucx, Objective Lucid, and JOOIP bound by the intensional (temporal) logic that is behind them. The distributed Java-based eduction (demand-driven) evaluation engine of the General Intensional Programming System (GIPSY) is the run-time system to cope with the scalability issues of the large evidential knowledge base. We then propose a near future work with the dataflow graph visualization and a toolset for compilation and execution of the Forensic Lucid programs. We show some examples by re-writing them in Forensic Lucid. We then postulate other investigations applications beyond the digital forensics domain.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".