Dynamic Real Time Framework for Abnormal Detection of IMS Core in Kubernetes Cloud
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
In the ever-evolving telecommunications sector, advancing from 5G towards 6G, maintaining the security of core infrastructures has become supreme. This study addresses the critical need for proactive and real-time anomaly detection within cloud-native environments. Leveraging cloud-native implementations within Kubernetes clusters, our framework utilizes advanced machine learning techniques to analyze data from applications and clusters. Specifically, this paper introduces a novel integration of k-means clustering and Long Short-Term Memory (LSTM) models for real-time anomaly detection in Kubernetes-based cloud-native environments, offering a unified framework capable of addressing both global and local anomalies across multiple layers of the IP Multimedia Subsystem (IMS) core. Existing research on anomaly detection in Kubernetes environments often focuses on specific application layers or isolated metrics, lacking a comprehensive solution that addresses the multidimensional and dynamic nature of IMS core anomalies across application, pod, and node levels in real-time. By employing k-means clustering and LSTM models, our approach achieves approximately 90% accuracy in anomaly detection. Extensive experiments with various model versions demonstrate the effectiveness of the framework, ensuring robust security and reliability for next-generation telecom networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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