A Deep Learning Approach for Detecting Virtual Link Anomalies in LEO Satellite Networks
Bibliographic record
Abstract
This paper proposes a deep learning (DL)-based time series (TS) anomaly detection method (DLTS) for the low earth orbit (LEO) satellite network slicing scenario, aiming to address the virtual link anomalies induced by software and hardware abnormalities. Initially, the time series anomalous variations of each resource utilization in satellite network slicing are categorized into three types based on the utilization of computing, storage, and network resources of virtual nodes. Thereafter, the anomaly detection problem is formulated as a classification problem, and the time series are transformed into images using the Gramian Angular Field (GAF) for model input. Lastly, we propose a design principle for a time-constrained deep neural network architecture to mitigate training time, and design a DL model architecture to classify the TS transformation images of resource utilization for each virtual node. This aligns with the objective of the satellite network slicing scenario. Additionally, a new evaluation metric is introduced. Experimental results underscore the shorter training time of the proposed model, and affirm its efficacy, demonstrated through accuracy, F1 score, and the newly proposed evaluation metric.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".