FIGS: A Realistic Intrusion-Detection Framework for Highly Imbalanced IoT Environments
Bibliographic record
Abstract
The rapid growth of Internet of Things (IoT) environments has increased security challenges due to heightened exposure to cyber threats and attacks. A key problem is the class imbalance in attack traffic, where critical yet underrepresented attacks are often overlooked by intrusion-detection systems (IDS), thereby compromising reliability. We propose Feature-Importance GAN SMOTE (FIGS), an innovative, realistic intrusion-detection framework designed for IoT environments to address this challenge. Unlike other works that rely only on traditional oversampling methods, FIGS integrates sensitivity-based feature-importance analysis, Generative Adversarial Network (GAN)-based augmentation, a novel imbalance ratio (GIR), and Synthetic Minority Oversampling Technique (SMOTE) for generating high-quality synthetic data for minority classes. FIGS enhanced minority class detection by focusing on the most important features identified by the sensitivity analysis, while minimizing computational overhead and reducing noise during data generation. Evaluations on the CICIoMT2024 and CICIDS2017 datasets demonstrate that FIGS improves detection accuracy and significantly lowers the false negative rate. FIGS achieved a 17% improvement over the baseline model on the CICIoMT2024 dataset while maintaining performance for the majority groups. The results show that FIGS represents a highly effective solution for real-world IoT networks with high detection accuracy across all classes without introducing unnecessary computational overhead.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".