| 基于支持向量机和自适应权重的网络安全态势评估模型 |
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| 引用本文： | 胡柳,周立前,邓杰,李瑞,赵正伟.基于支持向量机和自适应权重的网络安全态势评估模型[J].计算机系统应用,2018,27(7):188-192. |
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| 作者姓名： | 胡柳 周立前 邓杰 李瑞 赵正伟 |
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| 作者单位： | 湖南信息职业技术学院 计算机工程学院, 长沙 410200,湖南工业大学 计算机学院, 株洲 412007,湖南信息职业技术学院 计算机工程学院, 长沙 410200,河北省眼科医院 信息科, 邢台 054000,广西民族大学 信息科学与工程学院, 南宁 530006 |
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| 基金项目： | 湖南省自然科学基金（2016JJ5036） |
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| 摘 要： | 网络安全是近年来国内的重点研究领域，没有网络安全就没有国家安全.针对网络安全中数据源多样性、复杂性等特点，提出了一种基于支持向量机及自适应权重的网络安全态势评估模型.该模型由训练和预测模块组成，训练模块采用先验知识方法获取网络安全关注的重点数据，结合支持向量机和权重策略寻求评估模型.预测模块进行实时网络安全态势评估.通过实验过程和结果分析，表明该模型较好的支持小型网络安全态势的实时预测评估.
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| 关 键 词： | 网络安全 支持向量机 安全态势 评估模型 |
| 收稿时间： | 2017/6/15 0:00:00 |
| 修稿时间： | 2017/7/12 0:00:00 |
Evaluation Model of Network Security Situation Based on Support Vector Machine and Self-Adaptive Weight |
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| HU Liu,ZHOU Li-Qian,DENG Jie,LI Rui and ZHAO Zheng-Wei.Evaluation Model of Network Security Situation Based on Support Vector Machine and Self-Adaptive Weight[J].Computer Systems& Applications,2018,27(7):188-192. |
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| Authors: | HU Liu ZHOU Li-Qian DENG Jie LI Rui and ZHAO Zheng-Wei |
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| Affiliation: | College of Computer Engineering, Hunan College of Information, Changsha 410100, China,College of Computer, Hunan University of Technology, Zhuzhou 412007, China,College of Computer Engineering, Hunan College of Information, Changsha 410100, China,Information Section, Hebei Eye Hospital, Xingtai 054000, China and College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China |
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| Abstract: | Network security has become a priority research field in China in recent years, for there would be no national security without network security. In consideration of the characteristics of data source in network security like diversity and complexity, this study proposes an evaluation model of network security situation based on Support Vector Machine (SVM) and self-adaptive weight. The model is composed of training module and prediction module. The training module is used to obtain the key data concerned by network security through prior knowledge method and build evaluation model with a combination of SVM and weight strategy. The prediction module is used for real-time network security situation evaluation. Analysis of experimental process and results indicates that the proposed model can favorably support the real-time prediction and evaluation of the security situation of small-size networks. |
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| Keywords: | network security Support Vector Machine (SVM) security situation evaluation model |
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