Methods for Computer Network Security Management Assisted by Artificial Intelligence Models
Bibliographic record
Abstract
This work aims to construct a management system capable of automatically detecting, analyzing, and responding to network security threats, thereby enhancing the security and stability of networks. It is based on the role of artificial intelligence (AI) in computer network security management to establish a network security system that combines AI with traditional technologies. Furthermore, by incorporating the attention mechanism into Graph Neural Network (GNN) and utilizing botnet detection, a more robust and comprehensive network security system is developed to improve detection and response capabilities for network attacks. Finally, experiments are conducted using the Canadian Institute for Cybersecurity Intrusion Detection Systems 2017 dataset. The results indicate that the GNN combined with an attention mechanism performs well in botnet detection, with decreasing false positive and false negative rates at 0.01 and 0.03, respectively. The model achieves a monitoring accuracy of 98%, providing a promising approach for network security management. The findings underscore the potential role of AI in network security management, especially the positive impact of combining GNN and attention mechanisms on enhancing network security performance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".