Game Theory: Cyber Deception Based on the Redundancy of Diversified Honeypots
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Today, cyber deception is an essential element of proactive and reactive defense systems. Military equipment is increasingly connected to the Internet, and next-generation network security is becoming increasingly complex. This vast network of military tactics thus becomes exposed and vulnerable, opening a battlefield against cyberattacks. The security of such a network is therefore paramount. In this paper, we propose a game-theoretic cyber deception approach for the allocation of diversified honeypots. To ensure high availability of our security measure, we opt for diversified redundancy in honeypot placement. We will analyze the impact on the defender’s reward, with or without the attacker’s success probability. The optimization will be solved using the Nash balance. Regarding honeypot placement, we adopt a deployment with redundancy to guarantee the security of critical nodes. Our results show that redundancy strengthens the ability to diversify the honeypot, thus ensuring long-term network security.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it