Network Intrusion Detection Using a Stacking of AI-driven Models with Sampling
Bibliographic record
Abstract
Securing computer networks in Internet of Things (IoT) platforms has become increasingly challenging due to rising threats. Identifying unusual activities in IoT networks is crucial for maintaining their security. This research delves into detecting anomalies in network behavior to overcome security challenges. We use artificial intelligence (AI) models, specifically machine learning (ML) and deep learning (DL), for detecting intrusions in the network. We adopt four ML classifiers and implement two methods for assembling the classification outputs using stacking. Training our model on extensive historical network data enhances the ability to recognize abnormal network behaviors effectively. Initial tests on the NSL-KDD benchmark dataset have shown promising results, indicating the potential effectiveness of our approach. We also employed oversampling using Generative Adversarial Networks (GANs) to maintain balance in the data distribution, which led to noticeable improvement, reaching a 73.5% F-score and 61% accuracy compared to baseline models.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".