Differential Game Approach for Attack-Defense Strategy Analysis in Internet of Things Networks
Bibliographic record
Abstract
Internet of Things (IoT) is vulnerable to various cyber attacks due to the massive deployment of IoT devices and the openness of wireless environments. In this article, taking IoT devices as the network resources competed between an attacker and a defender, we study the modeling and analysis of network resource competition in an attack-defense game. The attacker and defender inject different competition strength in each IoT device as their strategies. As a result, the security state of each IoT device will change, which is captured by differential equations. To study the interaction between the attacker and defender and the evolution of the system security states, a zero-sum differential game is formulated by modeling the competition of IoT devices. To achieve the equilibrium of the formulated differential game, optimal control theory is employed to solve the optimization problems of players. Further, a Gauss–Seidel-like implicit finite-difference method is utilized to obtain the saddle point strategy. Finally, numerical results are provided to demonstrate the evolution of network resource competition between the attacker and defender. The results show that our formulated model can effectively and accurately characterize the evolution of the system security states with strategic interactions between the attacker and defender.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".