Compressing Attack Graphs Through Reference Encoding
Bibliographic record
Abstract
As a widely accepted model of multi-step network intrusions, attack graph has been applied to topological vulnerability analysis, network hardening, alert correlation, security metrics, and so on. A major challenge faced by attack graphs is the scalability: Even the attack graph of a moderate-sized network is typically incomprehensible to the human eyes, whereas that of large enterprise networks usually has an unmanageable size. Such a complexity, however, is not entirely unavoidable. In this paper, we shall show that an attack graph may contain much redundancy due to the similarity between different hosts' configurations. We then present a novel representation of attack graphs based on reference encoding. Specifically, subnets of hosts with similar configurations are represented using reference hosts while textual rules are employed to describe minor differences. The compression process is lossless and the resultant attack graph can directly provide useful insights. The effectiveness of the proposed model is illustrated through a case study and simulation results.
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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 | 0.001 | 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 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".