Generative Adversarial Networks for Robust Anomaly Detection in Noisy IoT Environments
Bibliographic record
Abstract
The Internet of Things (IoT) enables us to collect and process vast amounts of data in real time. However, the security of IoT devices and networks is highly susceptible to cyber attacks that threaten data integrity and service availability. Furthermore, due to the diverse nature of data collected from numerous nodes in IoT systems and the disturbances occurring within them, detecting anomalous activities and compromised nodes is considerably more challenging than in conventional computer systems. Therefore, it is crucial to develop robust and dependable anomaly detection methods to identify and remove malicious and/or unwanted data, which ensures their exclusion from IoT-powered applications and data analytics. To achieve this, this paper proposes a Generative Adverserial Networks (GAN)-based anomaly detection for IoT systems. The proposed model enables the autoencoder - using the adversarial training of GAN - to learn a better representation of IoT data, making it robust against noisy and changing environments. Based on experiments with real-world IoT datasets, the proposed framework has shown to improve the accuracy of detecting malicious traffic in IoT and surpass state-of-the-art anomaly detection models.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 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".