Security Enhancement in Surveillance Cloud Using Machine Learning Techniques

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Lingamallu Raghu Kumar
C. Ashokkumar
Purnendu Shekhar Pandey
Sathish Kumar Kannaiah
Balajee J
M. I. Thariq Hussan


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%.

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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.


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