Supplementary material - An Optimized Gradient Boosting Framework for IoT Intrusion Detection: A Comprehensive Evaluation on the CICIoT2023 Dataset
Bibliographic record
Abstract
This repository provides the complete supplementary material for the study “An Optimized Gradient Boosting Framework for IoT Intrusion Detection: A Comprehensive Evaluation on the CICIoT2023 Dataset.” It includes three preprocessed feature-selected datasets — Binary, Eight-Class, and Thirty-Four-Class — derived from the CICIoT2023 benchmark using stratified undersampling and LightGBM-based feature selection. These datasets contain only the discriminative features used in the experiments and are ready for direct model reproduction without further preprocessing. Each classification level is accompanied by: A comprehensive Excel results file (*_Classification_FullResults.xlsx) summarizing class distributions, feature importance, model performance reports, confusion matrices, inference latency, and overall accuracy/F1 summaries. A reproducible experiment script (*_Classification_Experiment.py) that automates the entire workflow — including data loading, normalization, model training (XGBoost, LightGBM, CatBoost), and evaluation. All resources are released under the Creative Commons Attribution 4.0 International (CC BY).Original data were obtained from the CICIoT2023 dataset by the Canadian Institute for Cybersecurity (UNB).For full documentation and experiment notebooks, please visit the companion GitHub repository:🔗 GitHub Repo
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.053 |
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".