[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124620-en":3,"doc-seo-124620-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124620,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Security Enhancement in Surveillance Cloud Using Machine Learning Techniques - FCM-SVM Approach","Most industries moving to cloud environments require robust security for safe, protected surveillance cloud transactions. The document presents approaches to strengthen security for vital-location surveillance by processing surveillance data and improving cloud security through machine learning-based pixel classification. Support Vector Machines (SVM) and Fuzzy C-means Clustering (FCM) are used, and a third-level CloudSec module extends a conventional two-tier design to reduce disclosure risk. The proposed FCM-SVM model is evaluated against ANN, KNN, SVM, and Naive Bayes, achieving an average accuracy of 94.4%.","Security Enhancement in Surveillance Cloud Using Machine Learning Techniques  \n1. Dr. Lingamallu Raghu Kumar, 2. Dr. C. Ashokkumar, 3. Dr. Purnendu Shekhar Pandey, 4. Sathish Kumar Kannaiah, 5. Balajee J, 6. Dr. M. I. Thariq Hussan  \n1Assistant Professor  \nKG Reddy College of Engineering and Technology,  \nDepartment of Computer Science and Engineering,  \nChilkur Village, Moinabad Mandal, RR district 501504  \nTelangana, India  \n[lrgupta528@gmail.com](lrgupta528@gmail.com)  \n2Assistant Professor  \nDepartment of Computing Technologies,  \nSchool of Computing  \nSRM Institute of Science and Technology,  \nKattankulathur, Chennai-603203  \n[ashokkuc@srmist.edu.in](ashokkuc@srmist.edu.in)  \n3Associate Professor,  \nDepartment ofCSE,  \nKIET Group of Institutions,  \nGhaziabad, U.P 201206  \n[purnendu.pandey@kiet.edu](purnendu.pandey@kiet.edu)  \n4Associate Professor,  \nDepartment of Computer Science and Engineering,  \nKoneru Lakshmaiah Education Foundation,  \nVaddeswaram, Guntur, Andhra Pradesh-522502  \n[ksathish1980@gmail.com](ksathish1980@gmail.com)  \n5Associate professor,  \nDepartment of Computer Science,  \nMother Theresa Institute of Engineering and Technology,  \nPalamaner, Andhra Pradesh  \n[j.balajee@mtieat.org](j.balajee@mtieat.org)  \n[6](6 Professor & Head)[ Professor & Head](6 Professor & Head)  \nDepartment of Information Technology & CSE (IOT)  \nGuru Nanak Institutions Technical Campus  \nIbrahimpatnam, Hyderabad – 501506, Telangana  \n[thariqhussain@rediffmail.com](thariqhussain@rediffmail.com)  \nAbstract: 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  \ntiered design by adding a third level, the CloudSec module, to lower the risk of potential disclosure of surveillance data.In our work we evaluateshow 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% .  \nKeywords: Image processing, machine learning, cloud computing, and surveillance.  \nI. Introduction  \nThe Internet is utilised via a distributed computing method known as \"cloud computing\" to supply a variety of services (CC) . The services could be provided as simple computer programmes designed to complete certain tasks, a platform for sharing online infrastructure, or any other platform made especially for the distribution of software over the Internet. Platform as a Service (PaaS), Infrastructure as a Service  \n(IaaS), and Software as a Service are the three categories that can be used to categorise cloud computing services in accordance with the definition above (SaaS) . The standard cloud services have a few characteristics in common. A crucial element of cloud computing is service on demand, which enables the end-user to precisely specify how many resources are needed to fulfil their request. Applications, platforms, and infrastructures are allegedly made available through SaaS, PaaS, and IaaS services in response to consumer demand. The user's business data, which consists of personal information and business rules, are uploaded to all of the services. It is necessary to secure both personal data and business rules because doing so improves user satisfaction. Security must therefore be offered in SaaS, PaaS, and IaaS. The security level is very low not only in terms of ","cbCaimuXiea2WGMw","https://ap.wps.com/l/cbCaimuXiea2WGMw","pdf",622037,1,10,"English","en",105,"# Abstract\n# Keywords\n# I. 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The document emphasizes securing personal data and business rules to improve user satisfaction and reduce risk.\"},{\"question\":\"Which machine learning methods are used to improve surveillance cloud security?\",\"answer\":\"The document uses Support Vector Machines (SVM) and Fuzzy C-means Clustering (FCM) to classify image pixels more precisely and enhance security.\"},{\"question\":\"How does the proposed design reduce the risk of surveillance data disclosure?\",\"answer\":\"It extends a conventional two-tier design by adding a third level, the CloudSec module, to lower the risk of potential disclosure of surveillance data.\"}]","Security Enhancement in Surveillance Cloud Using Machine Learning Techniques - FCM-SVM Approach | PDF",1785893360,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"security-enhancement-in-surveillance-cloud-using-machine-learning-techniques-fcm-svm-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/security-enhancement-in-surveillance-cloud-using-machine-learning-techniques-fcm-svm-approach/124620/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is cloud security essential for surveillance cloud transactions?","Question",{"text":75,"@type":76},"Cloud environments must provide a safe and protected setting for cloud-based transactions. 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