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Evaluation of computer network data security based on a deep learning algorithm
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DOI：10.1515/jisys-2024-0346.png)
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Abstract
En 中文
With the rapid development of information technology and the widespread application of computer networks, data security issues have become increasingly prominent, especially in key industries such as finance and medical care, where the protection of sensitive data has become a top priority. In order to meet these challenges, this article proposes a data security protection system based on deep learning (DL) technology, which can monitor and identify potential security threats in real time and greatly enhance data security by automatically analyzing and learning network traffic, user behavior, and intrusion characteristics. The study uses a deep belief network to complete high-intensity protection of personal or corporate private data while automatically countering and reminding some network intrusion behaviors. The performance differences in data security of individuals or enterprises before and after the use of the protection system were compared through experiments. The results show that the new system can improve data security performance by about 54.6% in all aspects. The results show that the new security protection model based on DL technology not only improves detection accuracy and response speed but also effectively resists emerging threats, providing a more solid protection barrier for network data.
Keywords:
network data security
deep learning
open computer
artificial intelligence
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