A Self-Synchronizing Cyber Deception Framework via Infrastructure as Code Reflection
Bibliographic record
Abstract
As cyberattacks become more sophisticated, the us e of honeypots has emerged as an alternative to proactively collec t attackers' exploit strategies. However, conventional honeypots o ften lack realism and require much human effort to deploy and m aintain in modern IT infrastructures. This excessive cost in resou rces creates a major obstacle to their widespread use. To address these challenges, this paper proposes a self-synchronizing cyber d eception framework that upholds the declarative approach of I nfrastructure as Code (IaC), maintaining idempotency and consis tency while automatically generating a deceptive environment. O ur framework treats the target system's laC files as a blueprint to automatically generate and deploy a high-fidelity, “digital twin” deception environment. Our framework uses an automated pip eli ne to analyze the laC file, apply the predefined transformation ru les, and dynamically build new container images - replicating stru ctural elements while replacing the core application logic with a h oneypot. After deployment, the framework keeps the deceptive en vironment synchronized with the original system by automaticall y re-deploying the pipeline in response to any changes in the sour ce laC file, increasing fidelity and reducing management costs. T he implementation of this prototype successfully shows a reductio n in the manual effort required for deploying and maintaining de ception environments, presenting a scalable and sustainable fram ework for active defense. This provides a strong foundation for b uilding the next-generation defense mechanisms that can adapt to both evolving cyberattacks and changing infrastructure.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".