A Game-theoretic Approach for DDoS Attack Mitigation in IIoT Deterministic Networking
Bibliographic record
Abstract
Deterministic networking (DetNet) is a promising technology that will help achieve the objectives of Industrial Internet of Things (IIoT) by meeting the latency constraints of various applications including the control of remote robots and autonomous vehicles. Nonetheless, adversaries may see in this paradigm a new opportunity for denial of service (DoS) attacks. IIoT control systems are often vulnerable to attacks and can be infected to create botnets capable of launching distributed DoS attacks that target the latency of deterministic IIoT networks, namely delay attacks. On the other hand, the allocation of limited intrusion detection resources within DetNet infrastructures remains a challenge. Conventional attack detection and mitigation solutions do not take the attack strategies into consideration, neither the DetNet network requirements. In this paper, we leverage game theory to design a defense strategy that can be used by the IIoT infrastructure to optimally allocate its security resources. We define the game utility based on system latency, which is crucial for a DetNet network. The proposed approach will enable the DetNet network to mitigate the impact of attacks and increase its resilience. Our results show that the attack impact is reduced by 54% compared to conventional strategies that do not account for the DetNet latency requirements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".