How far an evolutionary approach can go for protocol state analysis and discovery
Bibliographic record
Abstract
Securing todays computer networks requires numerous technologies to constantly be developed, refined and challenged. One area of research aiding in this process is that of protocol analysis, the study of the methods with which networks communicate. Our specific area of interest, the interaction with different protocol implementations, is a crucial component of this domain. Our work aims to identify and highlight a protocols states and state transitions, while minimizing the required a priori knowledge known about the protocol and its different versions (implementations). To this end, our approach uses a Genetic Programming (GP) based technique in order to analyze a client or a server of a given protocol via interacting with it with minimum a priori information. We evaluate our system against another well-known system from the literature on two different protocols, namely Dynamic Host Configuration Protocol (DHCP) and File Transfer Protocol (FTP). We measure the performances of these two systems in terms of the similarities and differences seen in the state diagrams produced for the protocols under testing. Results show that, by using our approach, it is possible to identify the different versions of a given 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".