中国邮电高校学报(英文) ›› 2016, Vol. 23 ›› Issue (3): 29-36.doi:

• Artificial Intelligence • 上一篇    下一篇

User abnormal behavior analysis based on neural network clustering

郑瑞娟1,陈京1,张明川1,朱军龙2,吴庆涛1   

  1. 1. 河南科技大学
    2. 北京邮电大学
  • 收稿日期:2015-12-11 修回日期:2016-05-21 出版日期:2016-06-28 发布日期:2016-07-05
  • 通讯作者: 陈京 E-mail:15036775207@163.com
  • 基金资助:

    融合群体协作的信息中心网络智慧路由机制研究;融合群体智慧的云服务自律协同提供机制研究;面向认知物联网的自主认知与智慧决策机制研究;网络技术与服务计算;智慧物联网的自主认知与自律安全关键技术

User abnormal behavior analysis based on neural network clustering

  • Received:2015-12-11 Revised:2016-05-21 Online:2016-06-28 Published:2016-07-05
  • Contact: Jing Chen E-mail:15036775207@163.com
  • Supported by:

    Research on Smart Routing Machanism for Information-centric Networks Merging Colony Cooperation;Autonomic Collaborative Service Provision Mechansim for Cloud Service Merging Colony Intelligence;Research on Autonomic Cognition and Intelligent Decision-making Mechanism for Cognitive Internet of Things

摘要: It is the premise of accessing and controlling cloud environment to establish the mutual trust relationship between users and clouds. How to identify the credible degree of the user identity and behavior becomes the core problem? This paper proposes a user abnormal behavior analysis method based on neural network clustering to resolve the problems of over-fitting and flooding the feature information, which exists in the process of traditional clustering analysis and calculating similarity. Firstly, singular value decomposition (SVD) is applied to reduce dimension and de-noise for massive data, where Map-Reduce parallel processing is used to accelerate the computation speed, and neural network model is used for softening points. Secondly, information entropy is added to hidden layer of neural network model to calculate the weight of each attribute. Finally, weight factor is used to calculate the similarity to make the cluster more accuracy. For the problem of analyzing the mobile cloud user behaviors, the experimental results show that the scheme has higher detection speed and clustering accuracy than traditional schemes. The proposed method is more suitable for the mobile cloud environment.

关键词:

Abstract:

It is the premise of accessing and controlling cloud environment to establish the mutual trust relationship between users and clouds. How to identify the credible degree of the user identity and behavior becomes the core problem? This paper proposes a user abnormal behavior analysis method based on neural network clustering to resolve the problems of over-fitting and flooding the feature information, which exists in the process of traditional clustering analysis and calculating similarity. Firstly, singular value decomposition (SVD) is applied to reduce dimension and de-noise for massive data, where Map-Reduce parallel processing is used to accelerate the computation speed, and neural network model is used for softening points. Secondly, information entropy is added to hidden layer of neural network model to calculate the weight of each attribute. Finally, weight factor is used to calculate the similarity to make the cluster more accuracy. For the problem of analyzing the mobile cloud user behaviors, the experimental results show that the scheme has higher detection speed and clustering accuracy than traditional schemes. The proposed method is more suitable for the mobile cloud environment.

Key words:

anomaly analysis, information security, SVD, neural network, information entropy