What packets may come
Bibliographic record
Abstract
We consider the problem of monitoring an interactive device, such as an implementation of a network protocol, in order to check whether its execution is consistent with its specification. At rst glance, it appears that a monitor could simply follow the input-output trace of the device and check it against the specification. However, if the monitor is able to observe inputs and outputs only from a vantage point external to the device---as is typically the case---the problem becomes surprisingly difficult. This is because events may be bu ered, and even lost, between the monitor and the device, in which case, even for a correctly running device, the trace observed at the monitor could be inconsistent with the specification.In this paper, we formulate the problem of external monitoring as a language recognition problem . Given a specification that accepts a certain language of input-output sequences, we de ne another language that corresponds to input-output sequences observable externally. We also give an algorithm to check membership of a string in the derived language. It turns out that without any assumptions on the specification, this algorithm may take unbounded time and space. To address this problem, we de ne a series of properties of device specifications or protocols that can be exploited to construct e cient language recognizers at the monitor. We characterize these properties and provide complexity bounds for monitoring in each case.To illustrate our methodology, we describe properties of the Internet Transmission Control Protocol (TCP), and identify features of the protocol that make it challenging to monitor e ciently.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".