在智能城市安全分析中采用K-means集群算法,以及对城市形象的神话体验分析
1Institute for Mythological Studies, Shanghai Jiaotong University, Shanghai, China.
PloS one
|March 10, 2025
概括
本研究介绍了一个K-Means集群 (KMC) +决策树 (DT) 模型,用于评估智慧城市 (SC) 的信息安全风险. 该模型有效评估安全,并揭示城市形象,神话体验和城市发展之间的联系.
科学领域:
- 信息安全 信息安全
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 智能城市 (SC) 产生大量数据,需要强大的信息安全评估模型.
- 评估SC的安全性对于保护基础设施和公民数据至关重要.
- SCs对个人主观体验的影响,例如神话体验,需要进行调查.
研究的目的:
- 使用K-Means集群 (KMC) 和决策树 (DT) 算法构建和评估智能城市 (SC) 的信息安全模型.
- 评估KMC+DT模型在识别和量化SC信息安全风险方面的实际价值.
- 探索智能城市发展与个人神话体验之间的关系.
主要方法:
- 开发了一个信息安全分析模型,结合K-Means Clustering (KMC) 和决策树 (DT) 算法.
- 将模型应用于38个智能城市 (SC) 进行实际分析和可行性评估,使用ROC曲线.
- 与KMC+DT模型的性能进行了比较,对比了天真贝叶斯 (NB),逻辑回归 (LR),随机森林 (RF),支持向量机 (SVM) 和梯度增强机 (GBM) 算法,并补充了对神话体验的问卷调查.
主要成果:
- KMC+DT模型实现了0.921的AUC-ROC,表现优于NB和LR,性能与RF,SVM和GBM相提并论.
- 安全分析显示,基于不同的城市属性,风险水平存在显著差异.
- 对神话经验的分析表明,对不同活动的支持有所不同,高风险的城市倾向于现代活动,低风险的城市倾向于传统活动.
结论:
- 开发的KMC+DT算法模型有效评估智能城市 (SC) 的信息安全风险,并证明其实际实用性.
- 积极的城市形象和引人入胜的神话体验被确定为城市发展的驱动力.
- 该研究强调了智能城市的技术安全,城市规划和社会文化经验的相互联系.
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