From protocol specifications to flaws and attack scenarios: an automatic and formal algorithm
Bibliographic record
Abstract
Presents a new approach to the verification of authentication protocols. This approach is formal, fully automatic and does not necessitate any specification of any protocol property or invariant. It takes the protocol specification as the parameter and generates the set of flaws, if any, as well as the corresponding attack scenarios. This approach involves three steps. First, protocol roles are extracted from the protocol specification. Second, the intruder's abilities to perform communication and computation are generated from the protocol specification. In addition to the classical, known intruder computational abilities, such as encryption and decryption, we also consider those computations that result from different instrumentations of the protocol. The intruder's abilities are modeled as a deductive system. Third, the extracted roles as well as the deductive system are combined to perform the verification. The latter consists in checking whether the intruder can answer all the challenges uttered by a particular role. If that is the case, an attack scenario is automatically constructed. To exemplify the usefulness and efficiency of our approach, we illustrate it on the Woo and Lam (1994) authentication protocol.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".