Paper Title :Harnessing Machine Learning for Cloud Security Threat Identification
Author :Battula Hruthvik Sai, Molakaluri Datta Venkat Sankar Karthik, Prathapani Satya Bhanu Vikas, Anantha Raman G R, Dhavala Sivarama Bhargava Teja, Sathish Kumar Kannaiah
Article Citation :Battula Hruthvik Sai ,Molakaluri Datta Venkat Sankar Karthik ,Prathapani Satya Bhanu Vikas ,Anantha Raman G R ,Dhavala Sivarama Bhargava Teja ,Sathish Kumar Kannaiah ,
(2024 ) " Harnessing Machine Learning for Cloud Security Threat Identification " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 75-82,
Volume-12,Issue-8
Abstract : Cloud computing's meteoric rise has triggered a spending spree, with companies investing heavily in both
internal use and external services. But with great power comeCloud computing's rapid adoption has led to significant
investments in both proprietary and third-party services. However, this growth also introduces substantial security challenges
for businesses and users alike. Machine learning (ML) has become a crucial tool in enhancing cloud security. This paper
conducts a Systematic Literature Review (SLR) of 63 studies to explore the integration of ML in cloud security. It identifies
three primary research areas: detecting various cloud threats, including data breaches and distributed denial-of-service
(DDoS) attacks, which account for 16% and 14% of threats respectively. A range of 30 ML techniques is employed, with
hybrid and standalone models like Support Vector Machine (SVM) showing significant promise. About 60% of the reviewed
studies evaluate their models using 13 different performance metrics, such as the true positive rate and training time. The
KDD and KDD CUP'99 datasets are frequently used for training these models. This review highlights the evolving role of
ML in cloud security, suggesting that more advanced ML techniques will continue to emerge, fortifying the cloud against
ever-evolving threats.
Keywords - Cloud Security; Artificial Intelligence (AI) Methods; Data Protection Violations; DDoS Attacks; Support
Vector Machines (SVMs).
Type : Research paper
Published : Volume-12,Issue-8
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-21180
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Copyright: © Institute of Research and Journals
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Published on 2024-11-19 |
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