Towards Anomaly Detection using Multiple Instances of Micro-Cluster Detection
Why this work is in the frame
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Bibliographic record
Abstract
One of the resources used in anomaly detection on log data is graph based approaches. Connections between adjacent log entries, co-occurrence of attributes, and other relations can be easily represented using graphs. In this paper, using a state-of-the-art (SOTA) graph based anomaly detection method, we reproduce and show the limitations on publicly available log data. Then we introduce a novel method, MIMC, that improves on the detection rates without causing a considerable loss in the overall performance. In order to evaluate the performance of MIMC, we perform experiments over the same datasets used in SOTA. The results indicate that MIMC has merit as a graph-based anomaly detection system over different types of log data. We believe that this is an important achievement on the road to building an unsupervised and online approach.
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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 | 0.001 |
| 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