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Risk Prediction Model Based on Improved AdaBoost Method for Cloud Usersse

机译：基于改进的AdaBoost方法的云用户风险预测模型

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摘要

Considering the problem how to protect the cloud services from being destroyed by cloud users, the riskpredictionmodel based on improved AdaBoost method is proposed. The risk prediction is regarded as two-class classificationproblem, and the risk of new cloud users could be predicted by the attributes of historical cloud users. In order toimprove the result of predicted, AdaBoost method is adopted in this paper. The error rate of the last training is used to adjustthe sample distribution of the next training, which can make the next training have stronger ability of identificationfor the error-classified samples. At the same time the weight of each weak classifier is set. After all, the strong classifier isgenerated by combined the weak classifiers through voting, which can improve the overall result of classification. Consideringthe wrongly-predicted cost, AdaBoost method is improved. The method of cost-sensitive is added into the modelin order to minimal the misclassified-cost. Experiments show that the cost-sensitive AdaBoost method has better classificationresult than the traditional ones and it can predict the risk of the new cloud user effectively and protect the securityof the cloud services.
机译：针对如何保护云服务不被云用户破坏的问题，提出了一种基于改进的AdaBoost方法的风险预测模型。风险预测被视为两类分类问题，可以通过历史云用户的属性来预测新云用户的风险。为了提高预测结果，本文采用AdaBoost方法。上次训练的错误率用于调整下一次训练的样本分布，可以使下一次训练对错误分类的样本具有更强的识别能力。同时设置每个弱分类器的权重。毕竟，强分类器是通过投票将弱分类器组合而成的，可以提高整体分类结果。考虑到错误的成本预测，改进了AdaBoost方法。将成本敏感的方法添加到模型中，以最小化错误分类的成本。实验表明，成本敏感的AdaBoost方法具有比传统方法更好的分类效果，可以有效预测新云用户的风险，并保护云服务的安全性。

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