Stackelberg Game for Resilient Collaborative Cyber Threat Detection and Response in IoT Networks
Bibliographic record
Abstract
Cooperative Intrusion Detection Systems (IDS) are pivotal for bolstering Internet of Things (IoT) security by facilitating collective vigilance against intrusions. Given the diverse nature of IoT ecosystems, relying on a silo IDS proves inadequate due to limited scope and information. In collaborative cyber defense, one IDS may consult other IDSs about suspicious attacks and aggregates their feedback to make the final decision. One main challenge is that an IDS may have no incentive to participate in the collaborative cyber defense system because mutual benefit may not be achieved due to selfish IDS behavior - networks frequently sending consultation requests but not responding to incoming requests. This paper proposes an incentive-based cooperative IDS approach based on a Stackelberg game which enables the IDSs to achieve compatibility between their feedback and consultation rates, thereby eliminating the incentive to behave selfishly. The game model enhances the resilience of the collaborative defense system against potential selfish actors which may degrade its performance and efficiency. The results demonstrate the effectiveness of the proposed approach in discouraging IDSs from exhibiting selfish behavior in IoT networks.
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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 | 0.003 | 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 | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".