Fuzzy Inference System for Byzantine Fault Tolerance in IoT Security
Bibliographic record
Abstract
The Internet of Things (IoT) is progressing rapidly, transforming the way people interact with the world through improved connectivity.Nevertheless, as the number of devices surges, security has become a primary concern.Through a comprehensive review of literature, it has been identified that byzantine targets the physical layer of IoT systems.This attack is carried out when a compromised device spreads malicious information to other nodes in the network, leading to potentially compromising the entire system.To address this issue, this work introduces a Fuzzy-based Byzantine Fault Tolerance (F-BFT) mechanism derived from a Type-1 fuzzy system require less computational power and resources compared to complex fuzzy systems, this can be advantageous in IoT systems where processing capabilities are limited.Based on measurements of modeling error at roughly σ=0.05 and an accuracy rate of 99.5%, recall of 98.89%, and F-Score of 98.83%, it has been observed from the results that the type-1 fuzzy model is highly precise.When compared to the existing system, the average delay of the F-BFT detection algorithm was 4.7 % decreased, average throughput was 5.0 % (increased), and average communication complexity was reduced from O(m 2 ) to O(m) where m is the number of nodes in the network.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".