Enhanced SVM Model with Orthogonal Learning Chaotic Grey Wolf Optimization for Cybersecurity Intrusion Detection in Agriculture 4.0
Bibliographic record
Abstract
Smart agriculture, also known as Agriculture 4.0, integrates cutting-edge technology with conventional farming practices through the agricultural Internet of Things (IoT).Despite its numerous advantages, Agriculture 4.0 introduces additional cybersecurity risks due to the widespread deployment of IoT-based devices.One significant threat is Distributed Denial of Service (DDoS) attacks, which can compromise the availability and integrity of agricultural systems.This paper proposes an Enhanced Multiclass Support Vector Machine (EMSVM) model for detecting DDoS attacks in Agriculture 4.0.To improve classification accuracy, the EMSVM model incorporates a novel optimization method called Orthogonal Learning Chaotic Grey Wolf Optimization (OLCGWO) for parameter selection.The performance of the proposed methodology is evaluated using two realworld traffic datasets, CIC-DDoS2019 and TON_IoT, which contain various DDoS attack scenarios.The results demonstrate the effectiveness of the EMSVM model in both binary and multiclass classification contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".