文章基本信息

标题：Non-conventional Bio-Cryptic DOST Features for Private Cloud Secure Access Using Machine Learning Algorithms
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作者：Sudhakar Godi ; Kurra Rajasekhara Rao
期刊名称：Webology
印刷版ISSN：1735-188X
出版年度：2022
卷号：19
期号：1
页码：4835-4846
DOI：10.14704/WEB/V19I1/WEB19323
语种：English
出版社：University of Tehran
摘要：Security issues in cloud computing is always challenging task for the researchers and practitioners. Especially in private cloud security is one of the critical issues to grant access to the remote server. Biometric authentication process will be one of the best solutions to grant access for private cloud server. This paper proposes a novel integrated technique for the secure cloud access by considering Bio-cryptic with DOES features, designated as â€˜Bio-Cryptic DOSTâ€™ (BCDOST) method. The method is implemented in Matlab and trained with 6000 data samples and tested using 5000 biometric data samples that includes, finger, face, iris and palm biometric features. Overall, 98.7% has obtained on a K-fold cross-validation (k=5), and also the results was compared with the present DOST and 4 different customary strategies. supported the results, it's over that, the proposed methodology performed well with relation to accuracy and computation-time.
关键词：Biometrics;Cloud Computing;Machine Learning