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AuthorLi, Shudong
AuthorLi, Yuan
AuthorHan, Weihong
AuthorDu, Xiaojiang
AuthorGuizani, Mohsen
AuthorTian, Zhihong
Available date2022-10-27T06:57:55Z
Publication Date2021-12-01
Publication NameSimulation Modelling Practice and Theory
Identifierhttp://dx.doi.org/10.1016/j.simpat.2021.102391
CitationLi, S., Li, Y., Han, W., Du, X., Guizani, M., & Tian, Z. (2021). Malicious mining code detection based on ensemble learning in cloud computing environment. Simulation Modelling Practice and Theory, 113, 102391.‏
ISSN1569190X
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85113388644&origin=inward
URIhttp://hdl.handle.net/10576/35495
AbstractHackers 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.
Languageen
PublisherElsevier B.V.
SubjectCloud computing
Ensemble learning
Malicious mining code
Mining virus
Static analysis
TitleMalicious mining code detection based on ensemble learning in cloud computing environment
TypeArticle
Volume Number113


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