A Framework for Anomaly Detection in IoT Networks Using Conditional Generative Adversarial Networks
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Bibliographic record
Abstract
While anomaly detection and the related concept of intrusion detection are widely studied, detecting anomalies in new operating behavior in environments such as the Internet of Things (IoT) is an active field of research. Anomaly detection models trained on datasets that are likely imbalanced have poor results, but the ability of Generative Adversarial Networks (GANs) to emulate complex high-dimensional distributions seen in real-world data suggests that they may be effective for anomaly detection. This paper proposes a novel framework for detecting anomalies in IoT networks utilizing conditional GANs (cGANs) to build realistic distributions for a given feature set to overcome the issue of data imbalance. To this end, a single class cGAN (ocGAN) was utilized to learn the minority data class to balance the dataset. Then, the binary class cGAN (bcGAN) model generates augmented data for the binary balance dataset. The performance of the ocGAN and bcGAN models in binary and multiclass classification environments were evaluated using a feed-forward neural network (FFN) and tested on two network-based anomaly detection datasets and five IoT network-based anomaly detection datasets. The proposed models outperformed other anomaly detection models in the standard metrics of accuracy, precision, recall, and F1-score.
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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 it