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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".