A Comparative Study of Machine Learning Algorithms for Intrusion Detection in IoT Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The pervasive threat of cyberattacks jeopardizes the security and privacy of the Internet of Things (IoT) landscape, spanning devices to networks.To counter these attacks, research has been directed towards the development of effective and appropriate countermeasures.Intrusion Detection Systems (IDSs), particularly those leveraging Machine Learning (ML) techniques for expedited attack detection, are currently recognized as some of the most potent solutions for preserving the integrity of the IoT environment.This study was conducted with the objective of evaluating the efficacy of supervised Machine Learning techniques, specifically, Random Forest (RF), Decision Trees (DT), and XGBoost classifiers, in detecting attacks within the IoT network.Chi-square (Chi2) and Mutual Information served as the employed Feature Selection Techniques.The research utilized two recent datasets for model evaluation.In pursuit of an optimal solution and high IDS model accuracy, a comparison of different techniques was undertaken across each stage of the ML workflow.The performance of the algorithms was assessed using the Edge-IIoT and BoTNeTIoT datasets, and the results from the two were compared.The impact of each workflow step on the model's accuracy was also examined.According to the performance metrics, the best results were achieved with the Mutual Information and XGBoost combination, outperforming both the Random Forest and Decision Tree classifiers.This study thus contributes to the ongoing efforts to strengthen IoT security through enhanced intrusion detection techniques.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it