On Augmented Intelligence and Performance Anomaly Detection in Unlabeled OpenWiFi Data
Bibliographic record
Abstract
Performance degradation of OpenWiFi traffic is a significant problem while provisioning service to a dense area consisting of thousands of clients operating at the same time. Among various vulnerabilities of poor performance in Wireless Local Area Networks (WLANs), deviation of a traffic pattern from normal indicates probable presence of anomaly or outlier. Adoption of machine learning algorithms including unsupervised and supervised models, the complexity of detection of a anomalous traffic along with respective root cause is untangled with augmented machine learning to involve domain knowledge input. In this paper, to cope with unlabeled data in an OpenWiFi setting, the following systematic work flow is proposed to augment machine learning-based anomaly detection. First, a combination of two unsupervised clustering algorithms is used to segregate the anomalies from normal distribution of traffic. The anomalous instances are confirmed via domain knowledge input. Next, supervised models are trained to detect anomalies in a different domain (Wireless Sensor Networks) with labeled data. Following upon feature extraction to obtain the same number of dimensions in the OpenWiFi data as the labeled dataset, trained supervised classifiers are used to detect anomalies in the OpenWiFi system. Furthermore, the impact of oversampling methods have also been investigated. Through numerical results we show the impact of the proposed supervised models in terms of decision-making metrics and the shift in performances from the standpoint of imbalanced distribution of rare-class classification problem.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".