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Analysis of Influencing Factors and Countermeasures of Computer Network Security

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Application of Intelligent Systems in Multi-modal Information Analytics (MMIA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1233))

Abstract

Since the new century, with the gradual popularization of computer networks around the world, network security defense methods have become more sophisticated and comprehensive, but they are still unable to defend against increasingly sophisticated and increasingly globalized virus attacks. In the process of network use and promotion, the influence of viruses on the network and hackers’ attacks have become an important factor threatening network security. Especially when people use the Internet to pay funds, remittances and other online financial activities, network security has become a constant topic. Establishing an efficient network security incident response system is of great significance for the network to better play its role. Based on the research of network security monitoring, network attack defense, network data backup and recovery theory and technology, this paper studies the strategy model of network security incident response, and uses relevant theories and methods of software engineering, combined with programming languages, to achieve Related application modules of this model. This article implements a low-cost, high-performance multi-data backup and recovery system that works in conjunction with other security devices and systems in the network. The overall structure and workflow are analyzed and designed to achieve multi-point backup and Fast recovery, efficient synchronization strategy for remote data, etc. The experimental research found that the network security incident response system can effectively reduce the security risk of the internal network, with a security proportion of more than 92%.

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Correspondence to Meng Cui .

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Liu, Y., Cui, M. (2021). Analysis of Influencing Factors and Countermeasures of Computer Network Security. In: Sugumaran, V., Xu, Z., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2020. Advances in Intelligent Systems and Computing, vol 1233. Springer, Cham. https://doi.org/10.1007/978-3-030-51431-0_39

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