A study of the internet financial interest rate risk evaluation index system in cloud computing. (2019)

• Record Type: Journal Article
• Authors: Mu, Sheng-Dong; Tian, Yi-Xiang; Luo, Yiwei
• Is Part Of: International journal of information and computer security. Volume 11:Number 2(2019)
• Page Start: 103
• Page End: 119
• Publication Date: 2019
• Subjects: cloud computing -- internet finance -- ITFIN -- risk assessment -- rough set -- particle swarm optimisation -- PSO -- support vector machine -- SVM; Computer security -- Periodicals; Information systems management -- Security measures -- Periodicals; Computer networks -- Security measures -- Periodicals; Information technology -- Security measures -- Periodicals; 005.805
• Languages: English
• ISSNs: 1744-1765

Abstract

Cloud computing is a product of computer technologies combined with network technologies and it has been widely applied in China. Experts and scholars in all fields begin to make many studies of cloud computing infrastructure construction and effective resource utilisation. With ITFIN, people can enjoy financial services in dealing with various problems. However, one person can play many identities in the network. This phenomenon posed a severe challenge to ITFIN network security and has largely intensified the risks, including the operational risk, market selection risk and network and information security risk. ITFIN resolves the risks by establishing a reliable, reasonable and effective risk assessment model. We conducted theoretical and empirical analysis, then constructed an assessment model against China's ITFIN risk. The model integrates rough set and particle swarm optimisation support vector machine (PSO-SVM). Finally, the model was used to assess the ITFIN risk in China. The empirical research results indicate that the model can effectively reduce redundant data information with rough set theory. The theory also guarantees a reliable, reasonable and scientific model, enhance the classification effect of the model. The parameters of SVM model obtained by optimising with PSO can effectively avoid local optimum, improve the effect of the classification model. Overall, the model has good generalisation ability and learning ability.