Using admissible interference to detect denial of service vulnerabilities
Bibliographic record
Abstract
Meadows recently proposed a formal cost-based framework for analysis of denial of service. It was showed how some principles that have already been used to make cryptographic protocols more resistant to denial of service by trading off the cost to defender against the cost to the attacker can be formalized. The first contribution of this paper is to introduce a new security property called impassivity which intends to capture the ability of a protocol to achieve these goals in the framework of a generic value-passing process algebra called Security Process Algebra (SPPA) extended with local function calls, cryptographic primitives and special semantic features in order to cope with cryptographic protocols. More specifically, impassivity is defined as an information flow property founded on bisimulation-based non-deterministic admissible interference. A sound and complete proof method for impassivity is also provided. The method extends previous results presented by the authors on bisimulation-based non-deterministic admissible interference and its application to the analysis of cryptographic protocols. The method is illustrated throughout the paper on the TCP/IP connection protocol. A more substantial application to the 1KP secure electronic payment protocol is given in appendix. Keywords: Denial of service, Protocols, Admissible interference, Bisimulation, Equivalence-checking 1.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".