SMART NETWORKING APPROACH FOR AUTOMATED INCIDENT MANAGEMENT
Bibliographic record
Abstract
Modern digital infrastructures are becoming increasingly complex, resulting in a higher frequency of network incidents that require rapid and accurate responses.Traditional manual incident management methods often lead to delays, human errors, and inefficient resource utilization.To overcome these limitations, this work presents a Smart Networking Approach for Automated Incident Management that integrates intelligent monitoring, machine learning-based anomaly detection, and automated decision-making mechanisms.The system continuously analyzes network traffic, correlates events from multiple sources, and predicts potential incidents using real-time analytics.Once an anomaly is detected, an automation engine initiates context-aware responses such as traffic rerouting, node isolation, or alert generation to prevent service disruption.Experimental analysis shows that the proposed approach significantly reduces detection latency, enhances accuracy, and minimizes downtime, making it suitable for enterprise networks, cloud environments, and large-scale IoT systems.Overall, this smart networking framework offers a scalable, proactive, and efficient solution for modern incident management.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".