[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126245-en":3,"doc-seo-126245-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126245,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Detection of Distributed Denial of Service (DDoS) Attacks in Software Defined Networks (SDN) Using Machine Learning Algorithms - Master of Science Thesis","This Master of Science thesis investigates detecting Distributed Denial of Service (DDoS) attacks within Software Defined Networks (SDN) using machine learning algorithms. The work defines research objectives and a problem statement, then builds the required datasets by generating sizable collections of traffic and benign requests. It presents networking background, data preprocessing steps, and comparative model training approaches including K-Nearest Neighbors, Logistic Regression, Decision Tree, Naïve Bayes, and Random Forest, followed by integration, testing, limitations, and future directions.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nDetection of Distributed Denial of Service (DDoS) Attacks in Software Defined  \nNetworks (SDN) Using Machine Learning Algorithms.  \nA Thesis submitted in partial fulfillment of the requirements  \nFor the degree of Master of Science in  \nComputer Science  \nBy  \nPrayag Pradipkumar Kotadia  \nDecember 2023  \nThe Thesis of Prayag Pradipkumar Kotadia is approved.  \nDr. Robert McIlhenny Date  \nDr. Alex Modarresi  \nDate  \nDr. John Noga, Chair Date  \nCalifornia State University, Northridge  \nAcknowledgements  \nI would like to express my heartfelt gratitude to my committee chair, Dr. John Noga, Professor, California State University, Northridge’s College of Engineering and Computer Science. Professor, your invaluable guidance and support throughout the entire research process have  \nbeen crucial for the completion of this thesis.  \nAdditionally, I would like to express my gratitude to Dr. Alex Modarresi and Dr. Robert McIlhenny, who served on my committee, for their thoughtful comments and contributions, which substantially improved the caliber of this thesis. Your combined knowledge and commitment have greatly influenced this effort.  \nMy sincere appreciation goes out to my parents for their constant encouragement, support, and faith in my potential. My pillars of support during my academic journey have been their love and guidance. Their unshakable confidence in me is reflected in my thesis. From the bottom of my  \nheart, thank you.  \nTable of Contents  \nSignature Page................................................................................................................................ ii  \nAcknowledgements ........................................................................................................................ iii  \n[List of Figures ................................................................................................................................ vi](List of Figures ................................................................................................................................ vi)  \n[List of Tables ...............................](List of Tables ...............................).................................................................................................. ix  \nAbstract ........................................................................................................................................... x  \nChapter 1: Introduction ................................................................................................................... 1  \n1.1 Objective ............................................................................................................................... 3  \n1.2 Problem Statement ................................................................................................................ 4  \n1.2.1 Generating Sizable Dataset ............................................................................................ 5  \n1.2.2 Training ML models ...................................................................................................... 5  \nChapter 2: Related Works ............................................................................................................... 6  \nChapter 3: Networking.................................................................................................................... 8  \n3.1 Introduction ........................................................................................................................... 8  \n3.2 Attacks ................................................................................................................................ 10  \n3.3 Distributed Denial of Services (DDoS) Attack................................................................... 11  \nChapter 4: Dataset ......................................................................................................................... 13  \n4.1 Benig","cbCaia6k9mEePvrb","https://ap.wps.com/l/cbCaia6k9mEePvrb","pdf",2158863,6,1,56,"English","en",105,"# Chapter 1: Introduction\n## 1.1 Objective\n## 1.2 Problem Statement\n# Chapter 2: Related Works\n# Chapter 3: Networking\n## 3.1 Introduction\n## 3.2 Attacks\n## 3.3 Distributed Denial of Services (DDoS) Attack\n# Chapter 4: Dataset\n## 4.1 Benign Requests\n## 4.2 Traffic Requests\n# Chapter 5: Machine Learning\n## 5.1 Data preprocessing\n## 5.2 Model Training\n# Chapter 8: Integration and Testing\n# Chapter 9: Limitations and Future work\n# Chapter 10: Conclusion","[{\"question\":\"What is the main objective of the thesis?\",\"answer\":\"The thesis aims to detect Distributed Denial of Service (DDoS) attacks in Software Defined Networks (SDN) using machine learning algorithms.\"},{\"question\":\"How does the thesis construct the dataset for training?\",\"answer\":\"It includes generating a sizable dataset and preparing both benign requests and traffic requests, supported by dataset-related sub-sections.\"},{\"question\":\"Which machine learning models are trained and compared?\",\"answer\":\"The document lists K-Nearest Neighbors, Logistic Regression, Decision Tree, Naïve Bayes, and Random Forest within the model training section.\"}]","Detection of Distributed Denial of Service (DDoS) Attacks in Software Defined Networks (SDN) Using Machine Learning Algorithms - 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