A Feature-Aware Adaptive Ensemble Framework for IoT Intrusion Detection Systems
Bibliographic record
Abstract
Intrusion Detection Systems (IDS) are essential for Internet of Things (IoT) security, but single models often fail due to IoT data heterogeneity. While machine learning ensembles combine complementary strengths, conventional static weighting schemes, such as majority voting and temporal stacking, do not adapt to sample-specific features and may underperform in diverse IoT scenarios. To address these limitations, we propose a dynamic feature-weighting ensemble framework for intrusion detection in IoT networks that combines adaptive weighting with a selected set of complementary base models suited to different traffic patterns. The approach combines four complementary models: Gradient Boosted Trees (LightGBM), Bagging-based Random Forest (RF), Instance-based k-Nearest Neighbors (kNN), and Deep Feedforward Neural Networks (FNN). It dynamically adjusts their weights based on the active features of each incoming traffic flow, emphasizing models best suited to specific patterns (e.g., LightGBM for packet-header patterns, FNN for nonlinear TCP flag interactions). Evaluated on the CICIoT2023 dataset, the framework achieved 99.95% precision and 98.59% recall, resulting in a$73 \%-97 \%$reduction in false positives (FP) and a$9 \%-30 \%$reduction in false negatives (FN) compared to individual models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".