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智慧城市信息安全风险评估模型构建与实证研究
引用本文:邹凯,向尚,张中青扬,毛太田.智慧城市信息安全风险评估模型构建与实证研究[J].图书情报工作,2016,60(7):19-24.
作者姓名:邹凯  向尚  张中青扬  毛太田
作者单位:湘潭大学公共管理学院 湘潭 411105
基金项目:本文系国家社会科学基金项目"大数据环境下政务信息资源优化配置与服务模式创新研究"(项目编号:15BTQ051)研究成果之一。
摘    要:目的/意义] 面对智慧城市日益突出的信息安全问题,提出一个科学的风险评估模型,以快速对智慧城市进行风险分类并分析风险因素。方法/过程] 从智慧城市信息安全的5个层次和风险评估的4个主要因素出发,构建智慧城市信息安全风险指标框架,对智慧城市实例进行ward系统聚类形成分类属性,选择C4.5算法的决策树方法建立风险评估模型,并验证模型的科学性。结果/结论] 结果发现:安全管理制度、数据泄露与损坏威胁、安全意识薄弱性和设备安全漏洞是智慧城市之间差距最显著的风险因素,根据评估结果提出保障智慧城市信息安全的建议。

关 键 词:信息安全  风险评估  智慧城市  C4.5决策树  
收稿时间:2016-02-01

Model Construction and Empirical Study on Smart City Information Security Risk Assessment
Zou Kai,Xiang Shang,Zhangzhong Qingyang,Mao Taitian.Model Construction and Empirical Study on Smart City Information Security Risk Assessment[J].Library and Information Service,2016,60(7):19-24.
Authors:Zou Kai  Xiang Shang  Zhangzhong Qingyang  Mao Taitian
Institution:School of Public Administration, Xiangtan University, Xiangtan 411105
Abstract:Purpose/significance] Considering the increasingly prominent information security issues of smart city, a scientific risk assessment model is proposed, which could classify and analyze the risk factors rapidly. Method/process] From five connotations of smart city public service's information security and four main factors of risk assessment, it constructs a smart city public service's information security risk index framework. Using the ward method of systematical clustering calculates 12 examples of smart city to form classification properties. Choosing the C4.5 algorithm of decision tree method to establish a model of risk assessment, and verify that the model is scientific. Result/conclusion] It finds that the safety management system, data leak and the threat of damage, safety awareness' weakness and equipment security vulnerabilities is the most significant risk factors' gap between wisdom cities, according to the results of the assessment, making recommendations to protect the information safety of wisdom city.
Keywords:information security  risk assessment  smart city  C4  5 decision tree  
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