[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123756-en":3,"doc-seo-123756-105":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},123756,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",6,"Technology","Guarding the Cloud - An Effective Detection of Cloud-Based Cyber Attacks using Machine Learning Algorithms","Cloud computing has become widely adopted because it delivers reliability and scalability, but it also introduces security risks alongside dependencies such as connectivity and operational downtime. This paper addresses guarding the cloud by focusing on two common cloud threats: Distributed Denial-of-service (DDoS) and Man-in-the-Cloud (MitC) computing attacks. Machine learning methods including Decision Trees, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN) are applied to detect malicious behavior. Simulated attacks generate datasets used for training and evaluation, and results show strong capability to distinguish malicious activity from legitimate network traffic, with Decision Trees showing the most promising performance.","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 19 No. 18 (2023) |   \n[https://doi.org/10.3991/ijoe.v19i18.45483](https://doi.org/10.3991/ijoe.v19i18.45483)  \nPAPER  \nGuarding the Cloud: An Effective Detection of Cloud-Based Cyber Attacks using Machine Learning Algorithms  \nBlerim Rexha1, Rrezearta Thaqi1(􀀍), Artan Mazrekaj1, Kamer Vishi2  \n1Faculty of Electrical and Computer Engineering, University of Prishtina, Prishtina, Kosovo  \n2Department of Informatics, University of Oslo,  \nOslo, Norway  \n[rrezearta.thaqi@uni-pr.edu](rrezearta.thaqi@uni-pr.edu)  \nABSTRACT  \nCloud computing has gained significant popularity due to its reliability and scalability, making it a compelling area of research. However, this technology is not without its challenges, including network connectivity dependencies, downtime, vendor lock-in, limited control, and most importantly, its vulnerability to attacks. Therefore, guarding the cloud is the objective of this paper, which focuses, in a novel approach, on two prevalent cloud attacks: Distributed Denial-of-service (DDoS) attacks and Man-in-the-Cloud (MitC) computing attacks. To tackle the detection of these malicious activities, machine learning algorithms, namely Decision Trees, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN), are utilized. Experimental simulations of DDoS and MitC attacks are conducted within a cloud environment, and the resultant data is compiled into a dataset for training and evaluating the machine learning algorithms. The study reveals the effectiveness of these algorithms in accurately identifying and classifying malicious activities, effectively distinguishing them from legitimate network traffic. The finding highlights Decision Trees algorithm with most promising potential of guarding the cloud and mitigating the impact of various cyber threats.  \nKEYWORDS  \ncloud computing, decision trees, support vector machine, naive bayes, k-nearest neighbors, machine learning, attacks, security  \n1 INTRODUCTION  \nOver the past years, cloud computing has been one of the most popular and fast growing technologies. It provides a range of different services for various applications such as data storage, servers, databases, networking, and software [1] . As it is combined by Internet, distributed systems and virtualization, it allows the users  \nRexha, B., Thaqi, R., Mazrekaj, A., Vishi, K. (2023) . Guarding the Cloud: An Effective Detection of Cloud-Based Cyber Attacks using Machine Learning Algorithms. International Journal of Online and Biomedical Engineering (iJOE), 19(18), pp. 158–174. [https://doi.org/10.3991/ijoe.v19i18.45483](https://doi.org/10.3991/ijoe.v19i18.45483)[ ](https://doi.org/10.3991/ijoe.v19i18.45483)[Article submitted 2023-09-07. Revision uploaded 2023-10-24. Final acceptance 2023-10-26.](Article submitted 2023-09-07. Revision uploaded 2023-10-24. Final acceptance 2023-10-26.)  \n© 2023 by the authors of this article. Published under CC-BY.  \n158 International Journal of Online and Biomedical Engineering (iJOE) iJOE | Vol. 19 No. 18 (2023)  \nGuarding the Cloud: An Effective Detection of Cloud-Based Cyber Attacks using Machine Learning Algorithms  \nto access the information technology infrastructure and applications on demand through the Internet. Compared to locally deployed information technology applications and solutions, cloud computing is characterized by virtualization, dynamic and high scalability, on-demand deployment, and high flexibility.  \nCloud computing has changed the understanding and functionality of the applications. This includes stronger computing power at a lower cost and also the combination with artificial intelligence, Internet of Things, and machine learning to enhance the scope of applications.  \nThe growth in cloud-enabled services and cloud market is unprece","cbCaikAAJPBR1E2f","https://ap.wps.com/l/cbCaikAAJPBR1E2f","pdf",1643613,1,17,"English","en",105,"# Introduction\n## Background and cloud computing characteristics\n## Cloud security challenges and common attacks\n# Methodology\n## Attack scenarios and dataset preparation\n## Machine learning algorithms used\n# Experimental Results\n## Detection and classification performance\n# Conclusion\n## Decision Trees effectiveness and mitigation impact","[{\"question\":\"Which two cloud attacks does the paper focus on for detection?\",\"answer\":\"The paper focuses on Distributed Denial-of-service (DDoS) attacks and Man-in-the-Cloud (MitC) computing attacks.\"},{\"question\":\"Which machine learning algorithms are used to detect malicious cloud activity?\",\"answer\":\"Decision Trees, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN) are used for detection and classification.\"},{\"question\":\"How is the dataset for training and evaluation created?\",\"answer\":\"Experimental simulations of DDoS and MitC attacks are conducted in a cloud environment, and the resulting data are compiled into a dataset for training and evaluating the algorithms.\"}]","Guarding the Cloud - An Effective Detection of Cloud-Based Cyber Attacks using Machine Learning Algorithms | PDF",1785818357,43,{"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},"guarding-the-cloud-an-effective-detection-of-cloud-based-cyber-attacks-using-machine-learning-algorithms","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/guarding-the-cloud-an-effective-detection-of-cloud-based-cyber-attacks-using-machine-learning-algorithms/123756/",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","2026-08-04",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},"Which two cloud attacks does the paper focus on for detection?","Question",{"text":75,"@type":76},"The paper focuses on Distributed Denial-of-service (DDoS) attacks and Man-in-the-Cloud (MitC) computing attacks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are used to detect malicious cloud activity?",{"text":80,"@type":76},"Decision Trees, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN) are used for detection and classification.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the dataset for training and evaluation created?",{"text":84,"@type":76},"Experimental simulations of DDoS and MitC attacks are conducted in a cloud environment, and the resulting data are compiled into a dataset for training and evaluating the algorithms.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]