TY - JOUR
T1 - Malicious mining code detection based on ensemble learning in cloud computing environment
AU - Li, Shudong
AU - Li, Yuan
AU - Han, Weihong
AU - Du, Xiaojiang
AU - Guizani, Mohsen
AU - Tian, Zhihong
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Cloud computing
KW - Ensemble learning
KW - Malicious mining code
KW - Mining virus
KW - Static analysis
UR - http://www.scopus.com/inward/record.url?scp=85113388644&partnerID=8YFLogxK
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U2 - 10.1016/j.simpat.2021.102391
DO - 10.1016/j.simpat.2021.102391
M3 - Article
AN - SCOPUS:85113388644
SN - 1569-190X
VL - 113
JO - Simulation Modelling Practice and Theory
JF - Simulation Modelling Practice and Theory
M1 - 102391
ER -