Fusion Based Approach for Distributed Alarm Correlation in Computer Networks
Why this work is in the frame
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Bibliographic record
Abstract
We propose a new distributed alarm correlation and fault identification in computer networks. The managed network is divided into a disjoint management domains and each management domain is assigned a dedicated intelligent agent. The intelligent agent is responsible for collecting, analyzing, and correlating alarms emitted form emitted from its constituent entities in its domain. In the framework of Dempster-Shafer evidence theory, each agent perceives each alarm as a piece of evidence in the occurrence of a certain fault hypothesis and correlates the received alarms into a single alarm called local composite alarm, which encapsulates the agent's partial view of the current status of the managed system. While the alarm correlation process is performed locally, each intelligent agent is able to correlate its alarms globally. These local composite alarms are, in turn, sent to a higher agent whose task is to fuse these alarms and form a global view of operation status of the running network. Extensive experimentations have demonstrated that the proposed approach is more alarm loss tolerant than the codebook based approaches and hence shown its effectiveness in a usually noisy network environment.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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