摘要
为了提高电子商务网站安全分析精度,提出混合聚类算法的电子商务网站安全分析算法。首先采集电子商务网站安全分析数据,选取能够描述电子商务网站安全状态的特征,然后将超网络聚类算法和K-means聚类算法组合成混合聚类算法,并采用混合聚类算法根据特征设计电子商务网站安全状态划分的树型结构,建立电子商务网站安全分析模型,判断电子商务网站中的行为数据是否存在异常,以此完成最后电子商务网站安全分析。测试结果表明,所提方法的安全分析正确率高,而且分析结果十分稳定。
In order to improve the accuracy of e-commerce website security analysis,a hybrid clustering algorithm for e-commerce website security analysis is proposed.Firstly,the security analysis data of ecommerce websites are collected,and the features that can describe the security status of e-commerce websites are selected.Then,the hybrid clustering algorithm is combined with the hyper network clustering algorithm and K-means clustering algorithm.Then,the hybrid clustering algorithm is used to design the tree structure of the e-commerce website security state division according to the characteristics,and the ecommerce website security analysis model is established to judge the e-commerce website security Whether the behavior data in the business website is abnormal,in order to complete the final e-commerce website security analysis.The test results show that the safety analysis accuracy of the proposed method is high,and the analysis results are very stable.
作者
王洋
Wang Yang(Economic and Trade Branch,Yangling Vocational&Technical College,Yangling Shaanxi 712100,China)
出处
《科技通报》
2021年第5期26-30,共5页
Bulletin of Science and Technology
基金
2018年杨凌职业技术学院科学研究基金项目(编号:A2018075)
关键词
电子商务网站
安全分析
聚类算法
欧氏距离
树型结构
e-commerce website
security analysis
clustering algorithm
Euclidean distance
tree structure
