QoS Technology in Computer Communication Transmission Network Security under the Artificial Intelligence

This paper proposes improved models for bandwidth prediction and security protection in power communication networks.First, a queuing model integrating quality of service technology is developed for bandwidth forecasting.Second, a security model combining grey relational analysis is presented to handle network attacks.Comparative experiments validate the performance of the new models.The key findings show that the proposed bandwidth prediction model achieved 2.21 Mbit/s forecasting rate and 78.89% utilization, outperforming existing methods.The security model also demonstrated 26% and 20% improvement in recovery time after simulated network attacks, compared to traditional techniques.The research provides useful network optimization and security enhancement strategies.However, limitations include small dataset size and lack of model validation.Future work should evaluate the models on larger power network data.

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Publication Details

Journal
Tehnicki vjesnik - Technical Gazette
Published
2024-10-31
DOI
https://doi.org/10.17559/tv-20231023001049
Primary Topic
Advanced Computational Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

QoS Technology in Computer Communication Transmission Network Security under the Artificial Intelligence

Tehnicki vjesnik - Technical Gazette
Advanced Computational Techniques and Applications
article

QoS Technology in Computer Communication Transmission Network Security under the Artificial Intelligence

article en

Abstract

This paper proposes improved models for bandwidth prediction and security protection in power communication networks.First, a queuing model integrating quality of service technology is developed for bandwidth forecasting.Second, a security model combining grey relational analysis is presented to handle network attacks.Comparative experiments validate the performance of the new models.The key findings show that the proposed bandwidth prediction model achieved 2.21 Mbit/s forecasting rate and 78.89% utilization, outperforming existing methods.The security model also demonstrated 26% and 20% improvement in recovery time after simulated network attacks, compared to traditional techniques.The research provides useful network optimization and security enhancement strategies.However, limitations include small dataset size and lack of model validation.Future work should evaluate the models on larger power network data.

Tehnicki vjesnik - Technical GazetteVol. 31(6)
Shenyang University of Technology (CN), Shenyang University of Chemical Technology (CN)
Openalex Percentile: Top 83%
Advanced Computational Techniques and Applications
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