Clustering using an Autoassociator: A Case Study in Network Event Correlation.
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Bibliographic record
Abstract
An autoassociator is a feedforward neural network that has the same number of input and output units. The goal of the autoassociator is very simple; to reconstruct its input at the output layer. Despite their simplicity, autoassociators have previously been shown to be quite successful on the task of Novelty Detection applied to industrial and military domains. The purpose of this paper is to test their utility on the more general task of clustering. In particular, we apply a clustering version of the autoassociator to the domain of Network Event Correlation. The results suggest that autoassociators are indeed useful as clustering systems. They were able to successfully correlate similar types of network alerts and have the added advantage of being fast once trained, a crucial feature when used for Network Event Correlation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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