ASTREAM: Data-Stream-Driven Scalable Anomaly Detection With Accuracy Guarantee in IIoT Environment
Bibliographic record
Abstract
Intrusion detection exerts a crucial influence on securing the IIoT driven by anomaly detection approaches. Dissimilar with the static data, the intrusion detection data is in the form of a dynamic data stream possessing the properties of infiniteness, correlations, and data distribution change. However, these properties cause some issues for current anomaly detection approaches. Firstly, it is impractical to save the whole dataset due to the infiniteness. Secondly, the correlations are hardly considered. Thirdly, the data distribution change can’t be appropriately handled due to a lack of model update and change detection strategy. Thus, we propose ASTREAM (anomaly detection in datastreams), a novel anomaly detection approach that merges sliding window, model update, and change detection strategies into LSHiForest to achieve accurate and efficient anomaly detection with better scalability. ASTREAM has the following characteristics: (a) the sliding window can be utilized to handle the infiniteness of data streams; (b) the introduced PCA can consider the correlations between different attributes; (c) the change detection and model update can detect data distribution change in time and train the new model. Comprehensive experiments are implemented on the KDDCUP99 dataset to validate ASTREAM performance. Experiment results reveal that ASTREAM outperforms baselines in aspects of accuracy and efficiency and has better scalability.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".