Risk Identification Method for Cloud Computing Safety based on LSA-GCC and LSA-SAM

Abstract

This paper proposes a generalized cluster risk evaluation model by applying a data mining method to the cloud computing risk evaluation. The model maps data sets into a semantic space via singular value decomposition (SVD), uses a clustering algorithm to classify them and to extract the prototype vector of a particular category from clustering results, and assigns a definite weight to each category so as to set up an initial prototype vector model. The model is taken as the basis for risk evaluation of information system. After the data to be evaluated were mapped to the same semantic space, they are calculated with the prototype vector of each category, so as to obtain the similarity of the category, and the cumulative sum of the similarity with the weight of the corresponding category comes out. Finally, a mean value is calculated to obtain the risk value of the data to be evaluated, namely, the risk value of the occasion when the data is obtained. In this paper, the safety risk information is obtained from the operating system log and Web application server log of a virtual host; the Latent Semantic Analysis-based Generalized Cluster Classifier (LSA-GCC) is adopted and the MapReduce-based LSA-GCC and LSA-SAM parallel acceleration experiment is conducted. The experimental results show that in a cloud computing environment of large-scale parallel processing, the method used in this paper can identify the log events of a cloud computing system and conduct risk prompt rapidly.