NoCSNet: Network-on-Chip Security Assessment Under Thermal Attacks Using Deep Neural Network
Bibliographic record
Abstract
As the demand for high-performance computing continues to rise, Network-on-Chip (NoC) architectures play a crucial role in enabling efficient data transmission within complex systems. However, the sensitivity of NoCs to intentional thermal fluctuations opens doors to conducting Denial of Service (DoS) attacks that can alter the system’s reliability and security. In this paper, for the first time, we introduce NoCSNet as a novel database of NoC traffic collected under various network configurations and thermal attack scenarios. We also use Deep Neural Networks (DNNs) to analyze the collected traffic to enhance data transmission security in the presence of thermal DoS attacks. Through comprehensive experimentation and evaluation, we demonstrate the effectiveness of NoCSNet in capturing the security profile of NoC architectures, which can be actively used in protecting NoCs’ data integrity and stability against thermal DoS attacks. The experimental results indicate that among the MLP, LSTM, and RNN deep neural networks, the RNN approach provides the highest attack detection accuracy of 93.8%. We anticipate that the collected dataset will help the community develop a deeper understanding of the susceptibility of NoCs against thermal DoS attacks.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".