Proactive Network Management Through AI-Powered Cybersecurity Solutions
Bibliographic record
Abstract
Active cybersecurity requires smart systems that can predict, and counter attack before it damages network integrity. This study presents an AI-based model of proactive network management comprising a deep learning-based analysis of traffic, a graph-based anomaly detector, a predictive-based analytics framework, reinforcement learning to make an adapted decision, and an automated policy enforcer. The validation of the system was downloaded to the CICIDS2017 dataset under real-world conditions. The results of the experiments prove high performance in relation to the current models of intrusion detection reaching accuracy of 96.8 and precision and recall of 93.9 and 94.3, respectively, combined with F1 -score, 88.1 and mAP of 89.5. These results underscore the capability of the framework to provide predictive, adaptive and automated defense effectively cutting the false negative and increasing resiliency to zero-day and polymorphic threats. The results prove that active security with AI can become a transition to network management opportunities that do not depend on the timely response to threats but on the prospects of attack anticipation in the context of modern structures.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".