[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123317-en":3,"doc-seo-123317-105":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":20,"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},123317,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Application and Analysis of Machine Learning and Deep Learning Algorithms in Detection of DDoS Cyberattacks","A Distributed Denial-of-Service (DDoS) attack overwhelms a target’s bandwidth or computation resources, often via multiple systems, making reliable detection and mitigation difficult because attack behaviors vary. This study develops an AI-based Intrusion Detection System (IDS) for DDoS threat detection using Machine Learning, Deep Learning, and hybrid techniques. Models including Random Forest, Naïve Bayes, Logistic Regression, KNN, DNN, and LSTM are trained and tested on CIC-DDoS-2019 and CIC-IoT-2023 datasets. Data imbalance is addressed using four balancing methods, and model performance is evaluated with accuracy, precision, recall, F1 score, balanced accuracy, and AUC. Results compare ML and DL models across balancing strategies to identify improved solutions.","University of Texas Rio Grande Valley  \nScholarWorks @ UTRGV  \nTheses and Dissertations  \n7-1-2024  \nApplication and Analysis of Machine Learning and Deep Learning Algorithms in Detection of DDoS Cyberattacks  \nDipok Deb  \nThe University of Texas Rio Grande Valley  \nFollow this and additional works at: [https://scholarworks.utrgv.edu/etd](https://scholarworks.utrgv.edu/etd)  \n Part of the Mathematics Commons  \nRecommended Citation  \nDeb, D. (2024) . Application and Analysis of Machine Learning and Deep Learning Algorithms in Detection of DDoS Cyberattacks [Master's thesis, The University of Texas Rio Grande Valley] . ScholarWorks @ UTRGV. [https://scholarworks.utrgv.edu/etd/1587](https://scholarworks.utrgv.edu/etd/1587)  \nThis Thesis is brought to you for free and open access by ScholarWorks @ UTRGV. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of ScholarWorks @ UTRGV. For more information, [please contact william.flores01@utrgv.edu](please contact william.flores01@utrgv.edu).  \nAPPLICATION AND ANALYSIS OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS IN DETECTION OF DDOS CYBERATTACKS  \nA Thesis  \nby  \nDIPOK DEB  \nSubmitted in Partial Fulfillment of the  \nRequirements for the Degree of  \nMASTER OF SCIENCE  \nMajor Subject: Mathematics  \nThe University of Texas Rio Grande Valley  \nJuly 2024  \nAPPLICATION AND ANALYSIS OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS IN DETECTION OF DDOS CYBERATTACKS  \nA Thesis  \nby  \nDIPOK DEB  \nCOMMITTEE MEMBERS  \nDr. Mrinal Kanti Roychowdhury  \nCo-Chair of Committee  \nDr. Sanjeev Kumar  \nCo-Chair of Committee  \nDr. Hansapani Rodrigo  \nCommittee Member  \nDr. Josef Sifuentes  \nCommittee Member  \nJuly 2024  \nCopyright 2024 Dipok Deb All Rights Reserved  \nABSTRACT  \nDeb, Dipok, Application and Analysis of Machine Learning and Deep Learning Algorithms in Detection of DDoS Cyberattacks . Master of Science (MS), July 2024, 121 pp., 62 tables, 41 figures, 61 references.  \nA Distributed Denial-of-Service (DDoS) attack involves overwhelming a target system’s data bandwidth or computational resources, often using multiple attack systems, aiming to slow down or disable the targeted system. Detecting and mitigating DDoS attacks effectively remains challenging due to their varying characteristics. One of the promising approaches involves developing an AI based Intrusion Detection System (IDS) against cyberattacks. In this study, we aim to develop an AI based Intrusion Detection System (IDS) for DDoS threat detection using Machine Learning, Deep Learning, or hybrid techniques. Different Machine Learning (ML) and Deep Learning (DL) algorithms like Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), K-Nearest Neighborhood (KNN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM) have been used to build the AI based intrusion detection system. CIC-DDoS-2019 and CIC-IoT-2023 datasets were utilized in this work for training and testing the performance of the AI models. In this work, we also concentrated on addressing data imbalance issues, which arose from the presence of high volume of attack data compared to benign data. Four different data balancing techniques have been used to solve the data imbalance problem. The performance of ML and DL models was assessed using metrics such as accuracy, precision, recall, F1 score, balanced accuracy, and Area Under the ROC-Curve (AUC) score under four different balancing techniques. Lastly, we compared the performance of these ML and DL models with different balancing techniques to obtain a better solution.  \nTABLE OF CONTENTS  \nPage  \nABSTRACT ........................................................... iii  \n[LIST OF TABLES ...................................................... vi](LIST OF TABLES ...................................................... vi)  \n[LIST OF FIGURES ...................................................... ix](LIST OF FIGURES ...................................................... ix)  \n","cbCaii077UquMUl1","https://ap.wps.com/l/cbCaii077UquMUl1","pdf",2709598,1,138,"English","en",105,"# Abstract\n# List of Tables\n# List of Figures\n# Chapter I: Introduction\n## DDoS Attack\n## TCP-SYN Attack\n## Motivation\n## Objective\n# Chapter II: Literature Review\n# Chapter III: Methodology\n## Model Architecture and Envioronment\n## Dataset\n### CIC-DDoS-2019 dataset\n### CIC-IoT-2023 dataset\n## Dataset pre-processing\n## Class Imbalance\n### SMOTE\n### ADASYN\n### SMOTE-TOMEK LINKS","[{\"question\":\"What problem does the thesis address in cyber security?\",\"answer\":\"It targets detecting and mitigating DDoS attacks, which are hard to handle because their characteristics can vary widely.\"},{\"question\":\"Which machine learning and deep learning algorithms are used?\",\"answer\":\"The study uses Random Forest, Naïve Bayes, Logistic Regression, K-Nearest Neighborhood (KNN), Deep Neural Network (DNN), and Long Short-Term Memory (LSTM).\"},{\"question\":\"How is class imbalance handled and how is performance evaluated?\",\"answer\":\"Four data balancing techniques address imbalance between high-volume attack data and benign data. Performance is measured using accuracy, precision, recall, F1 score, balanced accuracy, and AUC under different balancing conditions.\"}]","Application and Analysis of Machine Learning and Deep Learning Algorithms in Detection of DDoS Cyberattacks | PDF",1785815903,348,{"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},"application-and-analysis-of-machine-learning-and-deep-learning-algorithms-in-detection-of-ddos-cyberattacks","",{"@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/application-and-analysis-of-machine-learning-and-deep-learning-algorithms-in-detection-of-ddos-cyberattacks/123317/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the thesis address in cyber security?","Question",{"text":75,"@type":76},"It targets detecting and mitigating DDoS attacks, which are hard to handle because their characteristics can vary widely.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning and deep learning algorithms are used?",{"text":80,"@type":76},"The study uses Random Forest, Naïve Bayes, Logistic Regression, K-Nearest Neighborhood (KNN), Deep Neural Network (DNN), and Long Short-Term Memory (LSTM).",{"name":82,"@type":73,"acceptedAnswer":83},"How is class imbalance handled and how is performance evaluated?",{"text":84,"@type":76},"Four data balancing techniques address imbalance between high-volume attack data and benign data. Performance is measured using accuracy, precision, recall, F1 score, balanced accuracy, and AUC under different balancing conditions.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},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"]