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A study of the internet financial interest rate risk evaluation index system in cloud computing

机译：云计算中的互联网金融利率风险评估指标体系研究

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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.
机译：云计算是计算机技术与网络技术相结合的产物，在中国已得到广泛应用。各领域的专家学者开始对云计算基础架构的建设和有效的资源利用进行许多研究。使用ITFIN，人们可以享受金融服务来解决各种问题。但是，一个人可以在网络中扮演许多身份。这种现象对ITFIN网络安全构成了严峻挑战，并在很大程度上加剧了风险，包括运营风险，市场选择风险以及网络和信息安全风险。 ITFIN通过建立可靠，合理和有效的风险评估模型来解决风险。我们进行了理论和实证分析，然后构建了针对中国ITFIN风险的评估模型。该模型集成了粗糙集和粒子群优化支持向量机（PSO-SVM）。最后，该模型用于评估中国的ITFIN风险。实证研究结果表明，该模型能有效地利用粗糙集理论减少冗余数据信息。该理论还保证了模型的可靠，合理和科学，增强了模型的分类效果。通过PSO优化得到的SVM模型参数可以有效避免局部最优，提高分类模型的效果。总体而言，该模型具有良好的泛化能力和学习能力。

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