Merging Attack Trees and MITRE ATT&CK Tactics for More Effective Attack Modeling
Bibliographic record
Abstract
At present, attack trees/graphs and MITRE ATT &CK Navigator representations are considered to be the most effective ways of visually depicting a cyber attack. In this work, we first discuss the effectiveness of each of these approaches from the perspective of human perception and comprehension. We then propose a novel attack modeling technique that merges the traditional attack-trees with the MITRE ATT&CK methodology. We name this technique Color Coded MITRE ATT&CK Inspired Attack Tree (CC-MATT-AT) modeling. CC-MATT-AT is able to produces attack representations that are visually far superior than models produced by either attack trees or MITRE ATT &CK alone - which becomes especially important and evident in the case of complex attacks. Finally, we demonstrate the practical use and potential advantages of our novel technique by presenting a CC-MATT -AT model of one of the best-known hybrid attacks (i.e., attacks that span both IT and OT environments) to date - Sutxnet.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| 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 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".