An Empirical Study on Learning Models and Data Augmentation for IoT Anomaly Detection
Bibliographic record
Abstract
Among many other security applications, anomaly detection is one of the biggest users of deep learning methods. This growing popularity is mainly driven by two common beliefs: (i) its ability to manage complicated patterns inside large datasets (given a large amount of data) and (ii) its no need of separate feature engineering (as it is done within the model learning). In this study, we question both of those beliefs and revisit the effectiveness of feature selection and data augmentation in the performance of popular deep-learning based anomaly detection approaches. Additionally, we study the impact of other important factors of any learning based anomaly detection approaches including learning models (both traditional Ml and deep learning), data balancing techniques, hyper parameter tuning, etc. on their performance. From this study, we first report that those common beliefs are not always true - which necessitates a framework that can evaluate the usefulness of features and data for specific use cases (varying the data and need). Then, we propose a new framework that can fill in this gap and assist the data users and anomaly detection tools to perform better by selectively choosing all the configurations (such as, features, models, hyper parameter, balanced data, augmented data). Finally, we demonstrate the effectiveness of our framework using two major IoT datasets.
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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 | 0.036 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".