Automatic Handling of Protocol Dependencies and Reaction to 0-Day Attacks with ScriptGen-based Honeypots
Bibliographic record
Abstract
Abstract. Spitzner proposed to classify honeypots into low, medium and high interaction ones. Several instances of low interaction exist, such as honeyd, as well as high interaction, such as GenII. Medium interaction systems have recently received increased attention. ScriptGen and Role-Player, for instance, are as talkative as a high interaction system while limiting the associated risks. In this paper, we do build upon the work we have proposed on ScriptGen to automatically create honeyd scripts able to interact with attack tools without relying on any a-priori knowl-edge of the protocols involved. The main contributions of this paper are threefold. First, we propose a solution to detect and handle so-called intra-protocol dependencies. Second, we do the same for inter-protocols dependencies. Last but not least, we show how, by modifying our initial refinement analysis, we can, on the fly, generate new scripts as new at-tacks, i.e. 0-day, show up. As few as 50 samples of attacks, i.e. less than one per platform we have currently deployed in the world, is enough to produce a script that can then automatically enrich all these platforms. 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".