Machine learning-driven intrusion detection for Contiki-NG-based IoT networks exposed to NSL-KDD dataset
Bibliographic record
Abstract
Wide adoption of Internet of Things (IoT) devices and applications encounters security vulnerabilities as roadblocks. The heterogeneous nature of IoT systems prevents common benchmarks, such as the NSL-KDD dataset, from being used to test and verify the performance of different Network Intrusion Detection Systems (NIDS). In order to bridge this gap, in this paper, we examine specific attacks in the NSL-KDD dataset that can impact sensor nodes and networks in IoT settings. Furthermore, in order to detect the introduced attacks, we study eleven machine learning algorithms and report the results. Through numerical analysis, we show that tree-based methods and ensemble methods outperform the rest of the studied machine learning methods. Among the supervised algorithms, XGBoost ranks the first with 97% accuracy, 90.5% Matthews correlation coefficient (MCC), and 99.6% Area Under the Curve (AUC) performance. Moreover, a notable research finding of this study is that the Expectation-Maximization (EM) algorithm, which is an unsupervised method, also performs reasonably well in the detection of the attacks in the NSL-KDD dataset and outperforms the accuracy of the Naïve Bayes classifier by 22.0%.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".