[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119369-en":3,"doc-seo-119369-105":30,"detail-sidebar-cat-0-en-105":90},{"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},119369,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",6,"Technology","Machine Learning Techniques for Detecting Distributed Denial of Service (DDoS) Attacks","This research investigates the application of machine learning techniques, specifically K Nearest Neighbor, Support Vector Machine, Logistic Regression, Random Forest, and Gaussian Naive Bayes, for detecting Distributed Denial of Service (DDoS) attacks. The study utilizes the CIC-IDS2017 dataset, characterized by 30 features, including packet count, byte count, duration, and more, to effectively train and evaluate five distinct machine learning algorithms. Performance metrics such as accuracy, precision, recall, and F1-score are employed to assess the efficacy of each model. The results demonstrate that all five models exhibit high performance, achieving nearly perfect scores across all metrics, indicating their robustness in identifying DDoS traffic. Notably, the SVM and Random Forest models emerge as particularly effective, suggesting their suitability for real-world deployment in high-security environments. Future work will focus on optimizing these models for real-time use and exploring their potential drawbacks in other applications, ultimately aiming to enhance their overall effectiveness in combating network security threats.","Kean University  \nKean Digital Learning Commons  \n\n| Center for Cybersecurity | Open Educational Resources |\n| --- | --- |\n| Spring 4-24-2025\u003Cbr>Machine Learning Techniques for Detecting Distributed Denial of Service (DDoS) Attacks\u003Cbr>Caesar Marte\u003Cbr>Follow this and additional works at: [https://digitalcommons.kean.edu/cybersecurity](https://digitalcommons.kean.edu/cybersecurity)\u003Cbr> Part of the Computer Engineering Commons |  |\n\nRecommended Citation  \nMarte, Caesar, \"Machine Learning Techniques for Detecting Distributed Denial of Service (DDoS) Attacks\"(2025) . Center for Cybersecurity. 42.  \nDOI: [https://www.keanresearchdays.com/student-poster-presentation-2025-feed/machine-learning](https://www.keanresearchdays.com/student-poster-presentation-2025-feed/machine-learning)techniques-for-detecting-distributed-denial-of-service-ddos-attacks  \nAvailable at: [https://digitalcommons.kean.edu/cybersecurity/42](https://digitalcommons.kean.edu/cybersecurity/42)  \nThis Conference Proceeding is brought to you for free and open access by the Open Educational Resources at Kean Digital Learning Commons. It has been accepted for inclusion in Center for Cybersecurity by an authorized administrator of Kean Digital Learning Commons. For more information, please contact [learningcommons@kean.edu](learningcommons@kean.edu).","cbCaimmofutDjBM9","https://ap.wps.com/l/cbCaimmofutDjBM9","pdf",528673,1,2,"English","en",105,"# Machine Learning Techniques for Detecting Distributed Denial of Service (DDoS) Attacks\n## Introduction\n## Methods and Materials\n## Results\n## Conclusion\n## References","[{\"question\":\"What machine learning algorithms were used in this study?\",\"answer\":\"The study employed K Nearest Neighbor, Support Vector Machine, Logistic Regression, Random Forest, and Gaussian Naive Bayes algorithms.\"},{\"question\":\"What dataset was used for the research?\",\"answer\":\"The research utilized the CIC-IDS2017 dataset, which contains network traffic features like packet count, byte count, and duration.\"},{\"question\":\"What performance metrics were used to evaluate the models?\",\"answer\":\"The models were evaluated using accuracy, precision, recall, and F1-score to assess their effectiveness in detecting DDoS attacks.\"}]","Machine Learning Techniques for Detecting Distributed Denial of Service (DDoS) Attacks | PDF",1785723951,5,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"machine-learning-techniques-for-detecting-distributed-denial-of-service-ddos-attacks","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/technology/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/machine-learning-techniques-for-detecting-distributed-denial-of-service-ddos-attacks/119369/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What machine learning algorithms were used in this study?","Question",{"text":74,"@type":75},"The study employed K Nearest Neighbor, Support Vector Machine, Logistic Regression, Random Forest, and Gaussian Naive Bayes algorithms.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What dataset was used for the research?",{"text":79,"@type":75},"The research utilized the CIC-IDS2017 dataset, which contains network traffic features like packet count, byte count, and duration.",{"name":81,"@type":72,"acceptedAnswer":82},"What performance metrics were used to evaluate the models?",{"text":83,"@type":75},"The models were evaluated using accuracy, precision, recall, and F1-score to assess their effectiveness in detecting DDoS attacks.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,108,111,116,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":109,"slug":110},50,"technology",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},7,"Healthcare",40,"healthcare",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},8,"Research & Report",30,"research-report",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":29,"slug":136},19,"General","general"]