A Game Theoretic Model for Strategic Defence Selection Against DDoS Attacks in IoT Networks
Bibliographic record
Abstract
The rapid integration of the Internet of Things (IoT) into various systems, driven by advanced sensor networks, has dramatically improved real-time data monitoring and overall management across multiple industries. However, this integration also exposes IoT networks to various security vulnerabilities, mainly Distributed Denial of Service (DDoS) attacks, which can severely disrupt many services. Therefore, it is necessary to develop robust defence strategies for IoT networks. Traditional security measures often need to consider the strategic aspects of cybersecurity, where quick and precise decision-making is crucial. Given the adversarial nature of the interactions between attackers and defenders, selecting the most effective defence strategy to maximize benefits remains a challenge. To address this issue, this paper introduces the DDoS Defence Strategy Model (DDSM), which strategically uses game theory to select optimal defence mechanisms in IoT networks. The model dynamically adapts defence strategies based on the intensity and characteristics of the attack, optimizing the deployment of high-interaction and low-interaction honeypots and rate-limiting mechanisms. The DDSM model uses a gradient-based approach to achieve Nash equilibrium, adapting to evolving attack patterns to ensure efficient resource utilization and reduce operational overhead. The simulation results confirm the effectiveness of the model in selecting optimal defence strategies and maximizing defensive payoffs. The DDSM game model is designed to find the best combination of defences for IoT networks against high- and low-volume DDoS attacks, ensuring the continued availability of critical 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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".