Security Enhancement in Surveillance Cloud Using Machine Learning Techniques

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Published: Mar 11, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i3s.6154
Keywords:
Image processing, machine learning, cloud computing, surveillance

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Lingamallu Raghu Kumar
Assistant Professor, KG Reddy College of Engineering and Technology, Department of Computer Science and Engineering, Chilkur Village, Moinabad Mandal, RR district 501504 Telangana, India
C. Ashokkumar
Assistant Professor, Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai-603203
Purnendu Shekhar Pandey
Associate Professor, Department of CSE, KIET Group of Institutions, Ghaziabad, U.P 201206
Sathish Kumar Kannaiah
Associate Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh - 522502
Balajee J
Associate professor, Department of Computer Science, Mother Theresa Institute of Engineering and Technology, Palamaner, Andhra Pradesh
M. I. Thariq Hussan
Professor & Head, Department of Information Technology & CSE (IOT), Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Hyderabad – 501506, Telangana

Abstract

Most industries are now switching from traditional modes to cloud environments and cloud-based services. It is essential to create a secure environment for the cloud space in order to provide consumers with a safe and protected environment for cloud-based transactions. Here, we discuss the suggested approaches for creating a reliable and safe environment for a surveillance cloud. When assessing the security of vital locations, surveillance data is crucial. We are implementing machine learning methods to improve cloud security to more precisely classify image pixels, we make use of Support Vector Machines (SVM) and Fuzzy C-means Clustering (FCM). We also extend the conventional two-tiered design by adding a third level, the CloudSec module, to lower the risk of potential disclosure of surveillance data.In our work we  evaluates how well our proposed model (FCM-SVM) performed against contemporary models like ANN, KNN, SVD, and Naive Bayes. Comparing our model to other cutting-edge models, we found that it performed better, with an average accuracy of 94.4%.

Article Details

How to Cite
Kumar, L. R. ., Ashokkumar, C. ., Pandey, P. S. ., Kannaiah, S. K. ., J, B. ., & Hussan, M. I. T. . (2023). Security Enhancement in Surveillance Cloud Using Machine Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 46–55. https://doi.org/10.17762/ijritcc.v11i3s.6154
Issue
Vol. 11 No. 3s (2023)
Section
Articles

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