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Research and Implementation on Power Analysis Attacks for Unbalanced Data
Joint Authors
Li, Xiuying
Fan, Xiaohong
Li, You
Chen, Dong
Duan, Xiaoyi
Ding, Ding
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-22
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
In the power analysis attack, when the Hamming weight model is used to describe the power consumption of the chip operation data, the result of the random forest (RF) algorithm is not ideal, so a random forest classification method based on synthetic minority oversampling technique (SMOTE) is proposed.
It compensates for the problem that the random forest algorithm is affected by the data imbalance and the classification accuracy of the minority classification is low, which improves the overall classification accuracy rate.
The experimental results show that when the training set data is 800, the random forest algorithm predicts the correct rate of 84%, but the classification accuracy of the minority data is 0%, and the SMOTE-based random forest algorithm improves the prediction accuracy of the same set of test data by 91%.
The classification accuracy rate of a few categories has increased from 0% to 100%.
American Psychological Association (APA)
Duan, Xiaoyi& Chen, Dong& Fan, Xiaohong& Li, Xiuying& Ding, Ding& Li, You. 2020. Research and Implementation on Power Analysis Attacks for Unbalanced Data. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1208455
Modern Language Association (MLA)
Duan, Xiaoyi…[et al.]. Research and Implementation on Power Analysis Attacks for Unbalanced Data. Security and Communication Networks No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1208455
American Medical Association (AMA)
Duan, Xiaoyi& Chen, Dong& Fan, Xiaohong& Li, Xiuying& Ding, Ding& Li, You. Research and Implementation on Power Analysis Attacks for Unbalanced Data. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1208455
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208455