Hackers increasingly tend to abuse and nefariously use cloud services by injecting malicious mining code. This malicious code can be spread through infrastructures in the cloud platforms and pose a great threat to users and enterprises. In this study, a method is proposed for detecting malicious mining code in the cloud platforms, which constructs a detection model by fusing the Bagging and Boosting algorithms. By randomly extracting samples and letting models vote together to decide, the variance of model detection can be reduced obviously. Compared with traditional classifiers, the proposed method can obtain higher accuracy and better robustness. The experimental results show that, for the given dataset, the values of AUC and F1-score can reach 0.992 and 0.987 respectively, and the standard deviation of AUC values under different data inputs is only 0.0009.

黑客越来越倾向于通过注入恶意挖掘代码来滥用和恶意使用云服务。这种恶意代码可以通过云平台中的基础设施进行传播，对用户和企业构成巨大威胁。本研究提出了一种云平台恶意挖矿代码检测方法，该方法融合了Bagging算法和Boosting算法，构建了检测模型。通过随机抽取样本，让模型共同投票决定，可以明显降低模型检测的方差。与传统分类器相比，该方法可以获得更高的准确率和更好的鲁棒性。实验结果表明，对于给定的数据集，AUC和F1-score的值分别可以达到0.992和0.987，