Employing Hybrid ANOVA-RFE with Machine and Deep Learning Models for Enhanced IoT and IIoT Attack Detection and Classification
Bibliographic record
Abstract
The Internet of Things (IoT) has become an integral component in various applications, with significant prominence in healthcare and cybersecurity sectors.It is indispensable in medical diagnostics, monitoring, decision-support systems, and the safeguarding of sensitive data.However, the traditional methodologies have shown limitations in their ability to detect and classify all types of attacks effectively.This study presents a robust feature selection model, ANOVA-Recursive Feature Elimination (ANOVA-RFE), implemented with both Machine and Deep Learning paradigms, aiming to augment the security level by enhancing attack detection and classification.The models were trained using both the entire feature set and the selected features identified by ANOVA-RFE, demonstrating the efficiency and precision of the proposed method.The experiments yielded an accuracy of 100% and 99.96% using only the top five selected features from the first and second datasets, respectively.Furthermore, the performance of Gaussian Naive Bayes (GNB), K-Nearest Neighbors (K-NN), Random Forest (RF), AdaBoost (AB), Logistic Regression (LR), Decision Tree (DT), and Long Short-Term Memory (LSTM) models are evaluated, showcasing their respective accuracies on the first dataset.A scorelevel fusion was also employed, and the results were benchmarked against the current stateof-the-art, validating the robustness and high precision of the current study.Future work should consider analyzing different datasets and addressing further challenges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".