A Comparative Study of Machine Learning Algorithms for Intrusion Detection in IoT Networks
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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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".