[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123003-en":3,"doc-seo-123003-105":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},123003,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Using Machine Learning Algorithm for Detecting Distributed Denial of Service Attack","DDoS attacks, meaning Distributed Denial of Service attacks, directly threaten the reliability and availability of online services and networks. Because cybersecurity risks evolve continuously, machine learning approaches are used to detect DDoS behavior effectively. The study applies Exploratory Data Analysis to uncover patterns that indicate DDoS activity, then trains and tests multiple models including Random Forest, K-Nearest Neighbors, XGBoost, and Logistic Regression. Model performance is evaluated with accuracy, precision, recall, and F1-score. Results show strong detection performance, with Random Forest and XGBoost achieving particularly high accuracy, recall, and F1-score, while KNN and Logistic Regression perform comparatively differently.","Rochester Institute of Technology  \nRIT Digital Institutional Repository  \nTheses  \n5-21-2024  \nUsing Machine Learning Algorithm for Detecting Distributed Denial of Service Attack  \nZainab Alblooshi [zaa7513@rit.edu](zaa7513@rit.edu)  \nFollow this and additional works at: [https://repository.rit.edu/theses](https://repository.rit.edu/theses)  \nRecommended Citation  \nAlblooshi, Zainab, \"Using Machine Learning Algorithm for Detecting Distributed Denial of Service Attack\"(2024) . Thesis. Rochester Institute of Technology. Accessed from  \nThis Thesis is brought to you for free and open access by the RIT Libraries. For more information, please contact [repository@rit.edu](repository@rit.edu).  \nUSING MACHINE LEARNING ALGORITHM FOR DETECTING DISTRIBUTED DENIAL OF SERVICE  \nATTACK  \nby  \nZainab Alblooshi  \nA Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Professional Studies: Data Analytics  \nDepartment of Graduate Programs & Research  \nRochester Institute of Technology  \nRIT Dubai  \nMay 21, 2024  \nRIT  \nMaster of Science in Professional Studies:  \nData Analytics  \nGraduate Thesis Approval  \nStudent Name: Zainab Alblooshi  \nGraduate Capstone Title: USING MACHINE LEARNING ALGORITHM FOR DETECTING DISTRIBUTED DENIAL OF SERVICE ATTACK  \nGraduate Thesis Committee:  \nName: Dr. Sanjay Modak Date:  \nChair of committee  \n\n| Name: | Dr. Ehsan Warriach Mentor | Date: |\n| --- | --- | --- |\n\nAcknowledgments  \nI would like to express my heartfelt thanks to my thesis mentor, Dr. Ehsan, for his constant assistance, perceptive input, and valuable direction throughout the process of writing and researching my thesis. His expertise and encouragement were vital in shaping this academic endeavor. This journey has been a significant and unforgettable period of my academic life, and I express gratitude to all those who contributed to its accomplishment.  \nAbstract  \nDDoS attacks, which stand for Distributed Denial of Service attacks, play a significant role in impacting the reliability and availability of online services and networks. Since no system is completely immune to cybersecurity threats, which evolve daily with new techniques, studying this topic is crucial for exploring machine learning methods that can effectively detect DDoS attacks. An approach has been utilized to conduct Exploratory Data Analysis (EDA) to identify patterns suggesting the presence of DDoS attacks. Multiple machine learning models have been employed, such as Random Forest, K-Nearest Neighbors (KNN), XGBoost, and Logistic Regression. These models have undergone training and testing to identify abnormal network activity linked to DDoS attacks. Performance analysis measures, such as accuracy, recall, F1-score, and precision, are used to assess the efficiency of each model. The ML-based solution has demonstrated excellent performance in detecting DDoS attacks, as evidenced by the accurately labeled network traffic examples that determine whether they are legitimate or malicious, resulting in a calculated accuracy from the test results. Moreover, among the models used, the Random Forest and XGBoost models show exceptional accuracy, recall, and F1-score measurements, with an accuracy rate over 99% . On the other hand, while KNN shows praiseworthy performance, Logistic Regression yields somewhat lower accuracy and recall ratings.  \nKey Words: Distributed Denial of Service (DDoS), Machine Learning, Cy- bersecurity, Random Forest, K-Nearest Neighbors (KNN), XGBoost, Logistic Regression.  \nTable of Contents  \nACKNOWLEDGMENTS ................................................................................................................................................II  \nABSTRACT ...................................................................................................................................................................... III  \nLIST OF FIGURES.........................................................................","cbCaiaRKSczQZnht","https://ap.wps.com/l/cbCaiaRKSczQZnht","pdf",1369696,1,69,"English","en",105,"# Acknowledgments\n# Abstract\n# List of Figures\n# List of Tables\n# Chapter 1: Introduction\n## 1.1 Problem Statement\n## 1.2 Background of the Problem\n## 1.3 Project Goals\n## 1.4 Aims and Objectives\n## 1.5 Research Methodology\n## 1.6 Limitations of the Study\n# Chapter 2: Literature Review\n## 2.1 Literature Review\n## 2.2 Key Takeaways\n# Chapter 3: Project Description","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis addresses the detection of Distributed Denial of Service (DDoS) attacks to protect the reliability and availability of online services and networks.\"},{\"question\":\"Which machine learning models are used for DDoS detection?\",\"answer\":\"Random Forest, K-Nearest Neighbors (KNN), XGBoost, and Logistic Regression are used, after Exploratory Data Analysis identifies DDoS-related patterns.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Performance is assessed using metrics including accuracy, precision, recall, and F1-score, based on trained and tested network traffic labeled as legitimate or malicious.\"}]","Using Machine Learning Algorithm for Detecting Distributed Denial of Service Attack | 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