Big data network security defense mode of deep learning algorithm

IF 1.1 Q3 COMPUTER SCIENCE, THEORY & METHODS Open Computer Science Pub Date : 2022-01-01 DOI:10.1515/comp-2022-0257
Ying Yu
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引用次数: 1

Abstract

Abstract With the rapid development and progress of big data technology, people can already use big data to judge the transmission and distribution of network information and make better decisions in time, but it also faces major network threats such as Trojan horses and viruses. Traditional network security functions generally wait until the network power is turned on to a certain extent before starting, and it is difficult to ensure the security of big data networks. To protect the network security of big data and improve its ability to defend against attacks, this article introduces the deep learning algorithm into the research of big data network security defense mode. The test results show that the introduction of deep learning algorithms into the research of network security model can enhance the security defense capability of the network by 5.12%, proactively detect, and kill cyber attacks that can pose threats. At the same time, the security defense mode will evaluate the network security of big data and analyze potential network security risks in detail, which will prevent risks before they occur and effectively protect the network security in the context of big data.
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大数据网络安全防御模式的深度学习算法
随着大数据技术的快速发展和进步,人们已经可以利用大数据来判断网络信息的传播和分布,及时做出更好的决策,但也面临着特洛伊木马、病毒等重大网络威胁。传统的网络安全功能一般要等到网络电源开启到一定程度后才能启动,难以保证大数据网络的安全性。为了保护大数据的网络安全,提高其防御攻击的能力,本文将深度学习算法引入到大数据网络安全防御模式的研究中。测试结果表明,将深度学习算法引入网络安全模型的研究中,可以使网络的安全防御能力提升5.12%,主动发现并消灭可能构成威胁的网络攻击。同时,安全防御模式将对大数据的网络安全进行评估,详细分析潜在的网络安全风险,防患于未然,有效保护大数据背景下的网络安全。
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来源期刊
Open Computer Science
Open Computer Science COMPUTER SCIENCE, THEORY & METHODS-
CiteScore
4.00
自引率
0.00%
发文量
24
审稿时长
25 weeks
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