Research on cloud computing users’ public safety trust model based on scorecard-random forest

Shengli ZHOU; Canghong JIN; Lifa WU; Zheng HONG

doi:10.11959/j.issn.1000-436x.2018085

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Research on cloud computing users’ public safety trust model based on scorecard-random forest

Papers | 更新时间：2024-06-05

- Research on cloud computing users’ public safety trust model based on scorecard-random forest
- Journal on Communications Vol. 39, Issue 5, Pages: 143-152(2018)
- 作者机构：
  
  1. 陆军工程大学指挥控制工程学院，江苏南京 210007
  2. 浙江警察学院信息技术系，浙江杭州 310053
  3. 浙江大学城市学院计算机与计算科学学院，浙江杭州 310015
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2017YFB0802900);The National Natural Science Foundation of China(U1509219);The Science ＆ Technology Development Project of Hangzhou(20162013A08)
- DOI：10.11959/j.issn.1000-436x.2018085
  CLC： TP393
- Online First：2018-05，
  
  Published：25 May 2018
- 稿件说明：
移动端阅览
Shengli ZHOU, Canghong JIN, Lifa WU, et al. Research on cloud computing users’ public safety trust model based on scorecard-random forest[J]. Journal on Communications, 2018, 39(5): 143-152.
DOI：

Shengli ZHOU, Canghong JIN, Lifa WU, et al. Research on cloud computing users’ public safety trust model based on scorecard-random forest[J]. Journal on Communications, 2018, 39(5): 143-152. DOI： 10.11959/j.issn.1000-436x.2018085.

摘要

传统云计算用户信誉的研究主要集中在对用户操作行为信誉评估，较少涉及用户发布文本信息的安全管理，并且存在指标筛选欠准确、信誉评估结果缺乏科学验证等问题，难以满足监管部门的实际需求。针对以上问题，提出基于评分卡—随机森林的云计算用户公共安全信誉模型。首先，利用Word2Vec和卷积神经网络进行公共安全标签分类；其次，采用评分卡方法，筛选强相关性指标；最后，结合随机森林算法，建立云计算用户公共安全信誉模型。实验分析表明，所建立的模型能够解决云计算公共安全监管中用户信誉指标筛选不准确和信誉区分准确性低等问题，能够有效识别有害用户，提高云计算用户监管效率。

Abstract

Traditional cloud computing trust models mainly focused on the calculation of the trust of users’ behavior.In the process of classification and evaluation

there were some problems such as ignorance of content security and lack of trust division verification.Aiming to solve these problems

cloud computing users’ public safety trust model based on scorecard-random forest was proposed.Firstly

the text was processed using Word2Vec in the data preprocessing stage.The convolution neural network (CNN) was used to extract the sentence features for user content tag classification.Then

scorecard method was used to filter the strong correlation index.Meanwhile

in order to establish the users’ public safety trust evaluation model in cloud computing

a random forest method was applied.Experimental results show that the proposed users’ public safety trust evaluation model outperforms the general trust evaluation model.The proposed model can effectively distinguish malicious users from normal users

and it can improve the efficiency of the cloud computing users management.

关键词

Keywords

references

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