[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128599-en":3,"doc-seo-128599-105":31,"detail-sidebar-cat-0-en-105":92},{"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},128599,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","COMPARISON OF MACHINE LEARNING TECHNIQUES FOR CLASSIFICATION OF DISTRIBUTED DENIAL OF SERVICE ATTACKS - BASED ON FEATURE ENGINEERING IN SDN-BASED NETWORKS","Distributed Denial-of-Service (DDoS) attacks pose a major cybersecurity risk to software-defined networks (SDNs). This study develops a detection approach combining feature engineering with machine learning to classify DDoS attacks in SDNs. A Kaggle-derived dataset is cleaned and normalized, then the Correlation-based Feature Selection (CFS) method identifies an optimal feature subset. Multiple classifiers (RF, Decision Tree, AdaBoost, k-NN, Gradient Boosting, XGBoost, LightGBM, CatBoost) are trained and compared, with XGBoost achieving the best overall metrics and enabling effective detection and identification.","COMPARISON OF MACHINE LEARNING TECHNIQUES FOR CLASSIFICATION OF DISTRIBUTED DENIAL OF SERVICE ATTACKS BASED ON FEATURE ENGINEERING IN SDN-BASED NETWORKS  \nMuhammad Ikhwananda Rizaldi*1), Didih Rizki Chandranegara2), Denar Regata Akbi3)  \n1. Universitas Muhammadiyah Ma lang, Indonesia  \n2. Universitas Muhammadiyah Ma lang, Indonesia  \n3. Universitas Muhammadiyah Ma lang, Indonesia  \nArticle Info  \nKeywords: Correlation-Based Feature Selection; DDoS Attacks; Feature Engineering; Machine Learning; Software-Defined Networking  \nArticle history:  \nReceived 7 June 2024  \nRevised 21 July 2024  \nAccepted 14 August 2024  \nAvailable online 1 September 2024  \nDOI :  \n[https://doi.org/10.29100/jipi.v9i3.5262](https://doi.org/10.29100/jipi.v9i3.5262)  \n* Corresponding author.  \nMuhammad Ikhwananda Rizaldi E-mail address:  \n[aldirizaldy977@webmail.umm.ac.id](aldirizaldy977@webmail.umm.ac.id)  \nABSTRACT  \nDistributed Denial-of-Service (DDoS) attacks present a noteworthy cybersecurity hazard to software-defined networks (SDNs). This investigation presents an approach that depends on feature engineering and machine learning to discern DDoS attacks in SDNs. Initially, the data set acquired from Ka ggle goes through cleansing and normalization procedures, and the optimal subset of features is determined by employing the Correlation-based Feature Selection (CFS) algorithm . Subsequently, the optimal subset of features is trained and evaluated utilizing diverse Machine Learning algorithms, specifically Random Forest (RF), Decision Tree, Adaptive Boosting (Ada Boost), K-Nearest Neighbor (k-NN), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). The outcomes demonstrate that XGBoost outperforms the other algorithms in various performance metrics (e.g., accuracy, precision, recall, F1, and AUC values). Furthermore, a comparative analysis was carried out among various models and algorithms, revealing that the technique proposed by the researchers yielded the most favourable outcomes and effectively detected and identified DDoS attacks in SDN. Consequently, this investigation provides a novel perspective and resolution for SDN security.  \n.  \nI. INTRODUCTION  \nThe landscape of network infrastructure has experienced a significant transformation due to the rapid  \nadvancement and widespread use of state-of-the-art technologies like cloud computing and big data. This transformation has resulted in a tremendous increase in network traffic and an unparalleled dependence on networks for various purposes. It is important to note that 2020 witnessed an extraordinary global crisis in the form of the COVID-19 pandemic, which compelled individuals and organizations to rely heavily on online platforms for work, education, and entertainment. As a result, this surge in online activity placed immense pressure on the stability and security of networks, thereby exacerbating the challenges associated with conventional networking models. It is becoming increasingly evident that these traditional models must be adequately equipped to effectively meet users' diverse and complex needs in today's era. In light of this urgent demand for innovation and adaptability, Software-Defined Networking (SDN) has arisen as the most favored and desired networking technology owing to its incomparable adaptability, programmability, dynamism, and simplicity. SDN embodies a fundamental alteration in the manner in which networks are administered and operated, granting organizations the capability to address the constantly evolving requirements of the contemporary digital environment in a proficient and efficacious manner [1] . The architectural configuration of SDN distinguishes between the control plane and the data plane, which encompasses the application, management, and data planes. The data plane comprises switches and routers that are programmed and managed by the control plane. Theinteraction between ","cbCaip6LGJ69s0QC","https://ap.wps.com/l/cbCaip6LGJ69s0QC","pdf",1013391,3,1,18,"English","en",105,"# Abstract\n# Introduction\n## SDN architecture and traffic growth\n## Security risks and DDoS threats in SDN\n## Motivation for feature engineering and ML-based detection","[{\"question\":\"How does the study prepare the dataset for DDoS detection?\",\"answer\":\"The dataset is obtained from Kaggle, then cleaned and normalized before feature selection is performed.\"},{\"question\":\"Which feature selection method is used to select the optimal feature subset?\",\"answer\":\"The Correlation-based Feature Selection (CFS) algorithm is used to determine the best subset of features.\"},{\"question\":\"Which machine learning model performs best in the reported comparisons?\",\"answer\":\"XGBoost outperforms the other evaluated algorithms across multiple performance metrics such as accuracy, precision, recall, F1, and AUC.\"}]","COMPARISON OF MACHINE LEARNING TECHNIQUES FOR CLASSIFICATION OF DISTRIBUTED DENIAL OF SERVICE ATTACKS - BASED ON FEATURE ENGINEERING IN SDN-BASED NETWORKS | PDF",1786002024,45,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"comparison-of-machine-learning-techniques-for-classification-of-distributed-denial-of-service-attacks-based-on-feature-engineering-in-sdn-based-networks","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/comparison-of-machine-learning-techniques-for-classification-of-distributed-denial-of-service-attacks-based-on-feature-engineering-in-sdn-based-networks/128599/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How does the study prepare the dataset for DDoS detection?","Question",{"text":76,"@type":77},"The dataset is obtained from Kaggle, then cleaned and normalized before feature selection is performed.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which feature selection method is used to select the optimal feature subset?",{"text":81,"@type":77},"The Correlation-based Feature Selection (CFS) algorithm is used to determine the best subset of features.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning model performs best in the reported comparisons?",{"text":85,"@type":77},"XGBoost outperforms the other evaluated algorithms across multiple performance metrics such as accuracy, precision, recall, F1, and AUC.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]