All Predict Wisest Decides: A Novel Ensemble Method to Detect Intrusive Traffic in IoT Networks
Bibliographic record
Abstract
Internet of things (IoT) networks confront vari-ous network intrusion threats due to massively interconnected nodes that form an extensive attack surface for adversaries. Machine learning (ML)-based approaches are widely investigated to address network intrusions. It becomes further challenging to achieve promising performance for multi-class classification so to identify each attack type rather than detection of the presence of intrusion, which involves binary classification. ML models perform divergent detection performance in each class, so it is challenging to select one ML model applicable to all classes prediction. With this in mind, we propose an innovative ensemble learning framework, namely All Predict Wisest Decides (APWD) that builds on training of multiple ML models and testing them independently so to obtain prediction performance for all classes. For each attack category, an expert (i.e., wisest) model that performs the best F1 score, accuracy, lowest false detection rate is determined according to individual model results. The aggregation module makes decisions relying upon the wisest model determined for each class. APWD is a generic framework, and the types of MLs and the number of MLs can be customized in APWD. Experiments under a popular public dataset, NSL-KDD verify the proposed approach APWD by demonstrating that APWD boosts overall accuracy to 0.797, comparing 0.772 by XGBoost, 0.758 by RF, and 0.584 by Adaboost. Moreover, in certain attack types R2L, APWD increases F1 score by a factor of 18, from 0.022 by RF to 0.421.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".