ASTREAM: Data-Stream-Driven Scalable Anomaly Detection With Accuracy Guarantee in IIoT Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> nomaly detection in data <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stream</u> s), 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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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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