[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122693-en":3,"doc-seo-122693-105":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},122693,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine Learning-Based Anomaly Detection in Cloud Virtual Machine Resource Usage - A Project Report","Anomaly detection plays a key role in cloud computing by highlighting unusual behaviours that can trigger software glitches, security breaches, and performance degradation. The work targets aberrant resource-utilization patterns in virtual machines, a practical setting where major threats such as distributed denial-of-service attacks emerge. DDoS can throttle server capabilities and constrain internet traffic resources available to legitimate customers. Machine learning methods including QSVM, Random Forest, and neural architectures such as MLP and Autoencoders are used to learn attack distinctions, with experiments on the optimized NSL-KDD dataset.","San Jose State University  \nSJSU ScholarWorks  \n\n| Master's Projects | Master's Theses and Graduate Research |\n| --- | --- |\n| Spring 2023\u003Cbr>Machine Learning-Based Anomaly Detection in Cloud Virtual Machine Resource Usage\u003Cbr>Tarun Mourya Satveli\u003Cbr>San Jose State University\u003Cbr>Follow this and additional works at: [https://scholarworks.sjsu.edu/etd_projects](https://scholarworks.sjsu.edu/etd_projects)\u003Cbr> Part of the Artificial Intelligence and Robotics Commons, and the Information Security Commons |  |\n\nRecommended Citation  \nSatveli, Tarun Mourya, \"Machine Learning-Based Anomaly Detection in Cloud Virtual Machine Resource Usage\" (2023) . Master 's Projects. 1278.  \nDOI: [https://doi.org/10.31979/etd.cbtz-nn4v](https://doi.org/10.31979/etd.cbtz-nn4v)  \n[https://scholarworks.sjsu.edu/etd_projects/1278](https://scholarworks.sjsu.edu/etd_projects/1278)  \nThis Master's Project is brought to you for free and open access by the Master's Theses and Graduate Research at SJSU ScholarWorks. It has been accepted for inclusion in Master's Projects by an authorized administrator of SJSU ScholarWorks. For more information, please contact [scholarworks@sjsu.edu](scholarworks@sjsu.edu).  \nMachine Learning-Based Anomaly Detection in Cloud Virtual Machine  \nResource Usage  \nA Project Report  \nPresented to  \nThe Faculty of the Department of Computer Science  \nSan José State University  \nIn Partial Fulfillment  \nOf the Requirements for the Degree  \nMaster of Science  \nby  \nTarun Mourya Satveli  \nMay 2023  \n© 2023  \nTarun Mourya Satveli  \nALL RIGHTS RESERVED  \nMACHINE LEARNING-BASED ANOMALY DETECTION IN  \nCLOUD VIRTUAL MACHINE RESOURCE USAGE  \nby  \nTarun Mourya Satveli  \nAPPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE  \nSAN JOSÉ STATE UNIVERSITY  \nMay 2023  \nDr. Robert Chun  \nDr. Faranak Abri  \nMr. Revanth K Maddula  \nDepartment of Computer Science  \nDepartment of Computer Science  \nSoftware Engineer, TikTok  \nACKNOWLEDGEMENTS  \nI would like to express our gratitude and appreciation to Dr. Robert Chun, my advisor, for his invaluable support and guidance throughout this research endeavor. The completion of this undertaking has been greatly aided by his insightful comments, constructive criticism, and constant encouragement.  \nI would also like to thank Dr. Faranak Abri and Mr. Revanth K Maddula, both members of my committee, for their insightful comments and suggestions, which helped us refine and enhance our work. I would like to acknowledge the assistance and resources provided by our university, which allowed us to conduct this research.  \nFinally, I would like to thank everyone who has contributed to this endeavor in any way, including our families and friends, who have always been there to offer support and encouragement.  \nAbstract  \nAnomaly detection is an important activity in cloud computing systems because it aids in the identification of odd behaviours or actions that may result in software glitch, security breaches, and performance difficulties. Detecting aberrant resource utilization trends in virtual machines is a typical application of anomaly detection in cloud computing (VMs) . Currently, the most serious cyber threat is distributed denial-of-service attacks. The afflicted server's resources and internet traffic resources, such as bandwidth and buffer size, are slowed down by restricting the server's capacity to give resources to legitimate customers.  \nTo recognize attacks and common occurrences, machine learning techniques such as Quadratic Support Vector Machines (QSVM), Random Forest, and neural network models such as MLP and Autoencoders are employed. Various machine learning algorithms are used on the optimised NSL-KDD dataset to provide an efficient and accurate predictor of network intrusions. In this research, we propose a neural network based model and experiment on various central and spiral rearrangements of the features for distinguishing between different types of attacks and support our approach of better preservat","cbCaibog8DIjccQe","https://ap.wps.com/l/cbCaibog8DIjccQe","pdf",3128108,1,72,"English","en",105,"# INTRODUCTION\n## Research questions\n## Research Aim\n## Motivation\n## Problem statement\n## Research Objectives\n## Thesis Organization\n# LITERATURE WORK","[{\"question\":\"Why is anomaly detection important in cloud computing systems?\",\"answer\":\"It helps identify unusual behaviours that may lead to software glitches, security breaches, and performance difficulties.\"},{\"question\":\"What threat scenario does the research specifically relate to?\",\"answer\":\"Distributed denial-of-service (DDoS) attacks, which slow down server and internet traffic resources by limiting capacity for legitimate customers.\"},{\"question\":\"Which machine learning approaches are used for attack recognition?\",\"answer\":\"Models such as Quadratic Support Vector Machines (QSVM), Random Forest, and neural network models including MLP and Autoencoders, evaluated on the NSL-KDD dataset.\"}]","Machine Learning-Based Anomaly Detection in Cloud Virtual Machine Resource Usage - 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