Dynamic Resource Management to Defend Against Advanced Persistent Threats in Fog Computing: A Game Theoretic Approach
Bibliographic record
Abstract
Fog computing has gained tremendous popularity due to its capability of addressing the surging demand on high-quality ubiquitous mobile services. Nevertheless, the highly virtualized environment in fog computing leads to vulnerability to cyber attacks such as advanced persistent threats. In this paper, we propose a novel game approach of cyber risk management for the fog computing platform. We adopt the cyber-insurance concept to transfer cyber risks from fog computing platform to a third party. The system model under consideration consists of three main entities, i.e., the fog computing provider, attacker, and cyber-insurer. The fog computing provider dynamically optimizes the allocation of its defense computing resources to improve the security of the fog computing platform which is composed of multiple fog nodes. Meanwhile, the attacker dynamically adjusts the allocation of its attack computing resources to increase the probability of successful attack. Additionally, to prevent from the potential loss due to the attacks, the provider also makes a dynamic decision on the subscription of cyber-insurance for each fog node. Thereafter, the cyber-insurer accordingly determines the premium of cyber-insurance for each fog node. To model this dynamic interactive decision making problem, we formulate a dynamic Stackelberg game. In the lower-level, we formulate an evolutionary subgame to analyze the provider's defense and cyber-insurance subscription strategies as well as the attacker's attack strategy. In the upper-level, the cyber-insurer optimizes its premium strategy, taking into account the evolutionary equilibrium at the lower-level evolutionary subgame. We analytically prove that the evolutionary equilibrium is unique and stable, and we investigate the Stackelberg equilibrium by capitalizing on tools from the optimal control theory. Moreover, we provide a series of insightful analytical and numerical results on the equilibrium of the dynamic Stackelberg game.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".