Game Theory and Reinforcement Learning Based Secure Edge Caching in Mobile Social Networks
Bibliographic record
Abstract
Edge caching has become one of promising technologies in mobile social networks (MSNs) to proximally provide popular contents for mobile users. However, since caching contents inevitably consume resources (e.g., power, bandwidth, storage, etc.), edge caching devices maybe selfish to cheat the content provider for earning service fees. In addition, due to the open access of edge caching devices, the edge caching service is vulnerable to various attacks, such as man-in-the-middle attack and content tamper attack, etc., resulting in the degradation of content delivery performance. To efficiently tackle the above problems, in this paper, we propose a secure edge caching scheme for the content provider and mobile users in MSNs. Specifically, we first develop a secure edge caching framework consisting of the content provider, multiple edge caching devices, and some mobile users. To motivate the participation of edge caching devices, Stackelberg game is exploited to model the interactions between the content provider and edge caching devices. The content provider serves as the game-leader to determine the payment strategy of secure caching service and each edge caching device is the game-follower to make the strategy on the quality of secure caching service. Especially, the zero payment mechanism is adopted to suppress the selfish behaviors of edge caching devices. Apart from this, for lack of the knowledge on interactions between the content provider and edge caching devices in dynamic network scenarios, we also employ the Q-leaning to derive the optimal payment strategy of the content provider and the security strategy of edge caching device. Extensive simulations are conducted, and results demonstrate that the proposed scheme can efficiently motivate edge caching devices to provide the content provider and mobile users with high-quality secure caching services.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".