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 (DS) and clustering accuracy than traditional schemes. The proposed method is more suitable for the mobile cloud environment.
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 (DS) and clustering accuracy than traditional schemes. The proposed method is more suitable for the mobile cloud environment.
基金
supported by the National Natural Science Foundation of China (U1404611, U1204614, 61370221)
in part by Program for Science& Technology Innovative Research Team in University of Henan Province (14IRTSTHN021)
in part by the Program for Science & Technology Innovation Talents in the University of Henan Province (14HASTIT045, 16HASTIT035)
in part by Henan science and technology innovation outstanding talent (164200510007)