[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118796-en":3,"doc-seo-118796-105":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},118796,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Nukhba Nukhba - ANOMALY DETECTION IN NETWORK MONITORING - A comparison of performance analysis of generic vs cloud-based Machine learning Platform","Anomaly detection techniques for telecommunications enable automated network supervision and improved reliability with reduced operational cost. This Master’s thesis compares a generic machine learning platform, scikit-learn, with Azure Machine Learning Studio using anomaly detection as the central use case. Both platforms apply multiple machine learning methods to two anomalously labeled network datasets and are evaluated across data processing, model creation and prediction, cost, and usability. Findings indicate cloud platforms offer stronger usability through low/no-code workflows, but reduced control and limited ready-to-use and unsupervised support; cloud deployment remains production-ready with minimal effort.","Nukhba Nukhba  \nANOMALY DETECTION IN NETWORK MONITORING  \na comparison of performance analysis of generic vs cloud-based Machine learning Platform  \nMaster of Science Thesis  \nFaculty of Information Technology and Communication Sciences Examiners: Ph.D. Tapio Elomaa, D.Sc. (Tech) Adrian Burian May 2023  \nABSTRACT  \nNukhba Nukhba: Anomaly Detection in Network Monitoring,  \na comparison of performance analysis of generic vs cloud-based Machine learning Platform  \nM. Sc. Thesis  \nTampere University  \nMaster’s Degree Programme in May 2023  \nApplications of anomaly detection in telecommunication industry are widely in demand for the purpose of automating network supervision and to provide better network reliability at reduced cost. To meet this increasing demand, various machine learning platforms have emerged that empower businesses to leverage ML capabilities at a better cost. This study compares a generic machine learning platform scikit-learn with Azure Machine learning studio, while taking anomaly detection as a centre use case. The focus of this thesis is to understand the value offered by the Cloud giants for the application of anomaly detection and possibly suggest a better platform for the development of machine learning application, deployment, and maintenance.  \nThis thesis studies a generic and cloud-based machine learning platform by employing various machine learning methods for anomaly detection. The machine learning methods from a generic platform and a cloud-based platform are applied onto two anomalous labelled network datasets. These platforms are analysed with respect to data processing, model creation and prediction, cost, and usability.  \nAs a result, this work concludes the comparison with various aspects of model creation, visualization of the results, model performance, cost, and ease of use. Results show that cloud-based machine learning platforms do provide an edge with respect to usability by providing low to no code option. However, low code also implies less control over the model, and it also comes with limited ready to use algorithms. And even less built-in support for unsupervised algorithms in Azure designer. The whole process of model creation, data processing and deployment was effortless, thus, it is easy to convert to production ready.  \nIn addition, cloud-based platforms eliminate the need of buying high end machine for storage computation. However, the studio charges money per experimentation hour and storage. Both platforms are well suited for the purpose of anomaly detection with each having some advantage over the other.  \nKeywords and terms: Anomaly detection, mobile networks, machine learning.  \nThe originality of this thesis has been checked using the Turnitin OriginalityCheck service.  \nPREFACE  \nThis degree project is carried out at Nokia Corporation, a Finnish multinational, telecommunications, information technology and consumer electronics corporation with global existence. Nokia Networks is one of the biggest subsidiaries of Nokia Corp and is a leading vendor in data networking, IP infrastructure, telecommunication equipment, software and related services used by communication service providers. The objective of this study is to analyse the application of anomaly detection system for network monitoring, as part of a project taking place inside Nokia Networks.  \nCONTENTS  \n1. INTRODUCTION.......................................................................................................................9  \n1.1 Structure of the Thesis..................................................................................................... 10  \n2. BACKGROUND ......................................................................................................................... 11  \n2.1 Anomaly Detection ............................................................................................................ 11  \n2.2 Measures for Anomaly Detection ....................................","cbCaivRz1LuGRb25","https://ap.wps.com/l/cbCaivRz1LuGRb25","pdf",1702969,1,55,"English","en",105,"# Introduction\n## Structure of the Thesis\n# Background\n## Anomaly Detection\n## Measures for Anomaly Detection\n## Machine learning Methods\n## Machine Learning as a Service\n## Research Objective\n# Related Work\n## Density based and Clustering techniques\n## Classification Based techniques\n## Supervised anomaly detection techniques\n# Methodology\n## Anomaly detection workflow\n## Data\n## Dimensionality reduction\n## Models\n## Unsupervised Learning K-means clustering","[{\"question\":\"What platforms are compared in this thesis for anomaly detection?\",\"answer\":\"The study compares a generic machine learning platform (scikit-learn) with Azure Machine Learning Studio, using anomaly detection as the main use case.\"},{\"question\":\"How are the platforms evaluated in the thesis?\",\"answer\":\"Evaluation covers data processing, model creation and prediction, cost, and usability, using two anomalous labeled network datasets.\"},{\"question\":\"What are the key conclusions about cloud-based platforms versus generic tools?\",\"answer\":\"Cloud-based platforms provide an advantage in usability via low/no-code options and reduce hardware needs, but they also limit model control and offer restricted ready-to-use and unsupervised algorithm support.\"}]","Nukhba Nukhba - 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