[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125593-en":3,"doc-seo-125593-105":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":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},125593,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","A Comparative Study for SDN Security Based on Machine Learning","Traditional networks struggle with stable configurations when new nodes must join the system in modern environments, motivating the shift toward Software Defined Networks (SDN). The study compares Deep Neural Network (DNN) and classical Machine Learning (ML) methods using multiple feature selection strategies. ML classifiers include decision tree, Naïve Bayes, and Support Vector Machine. Experiments evaluate performance on the NSL–KDD dataset with 41 features and 148,517 samples. Results show decision tree as the most accurate and effective approach, outperforming earlier studies across evaluation metrics.","A Comparative Study for SDN Security Based on Machine Learning  \n[https://doi.org/10.3991/ijim.v17i11.39065](https://doi.org/10.3991/ijim.v17i11.39065)  \nKhattab M Ali Alheeti1(􀀍), Abdulkareem Alzahrani2, Maha Alamri2,  \nAythem Khairi Kareem3, Duaa Al_Dosary 1  \n1 Computer Networking Systems Department, University of Anbar, Anbar, Iraq  \n2 Faculty of Computer Science and Information Technology, Al Baha University, Al Baha, Saudi Arabia  \n3 Department of Heet Education General Directorate of Education in Anbar, Ministry of  \nEducation, Heet, Anbar, Iraq  \n[co.khattab.alheeti@uoanbar.edu.iq](co.khattab.alheeti@uoanbar.edu.iq)  \nAbstract—In the past decade, traditional networks have been utilized to transfer data between more than one node. The primary problem related to formal networks is their stable essence, which makes them incapable of meeting the requirements of nodes recently inserted into the network. Thus, formal networks are substituted by a Software Defined Network (SDN) . The latter can be utilized to construct a structure for intensive data applications like big data. In this paper, a comparative investigation of Deep Neural Network (DNN) and Machine Learning (ML) techniques that uses various feature selection techniques is undertaken. The ML techniques employed in this approach are decision tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM) . The proposed approach is tested experimentally and evaluated using an available NSL–KDD dataset. This dataset includes 41 features and 148,517 samples. To evaluate the techniques, several estimation measurements are calculated. The results prove that DT is the most accurate and effective approach. Furthermore, the evaluation measurements indicate the efficacy of the presented approach compared to earlier studies.  \nKeywords—Software Defined Network (SDN), Deep Neural Network (DNN), Machine Learning (ML), NSL-KDD  \n1 Introduction  \nIn wireless communication networks, sensor nodes in wireless sensor networks are considered as the fundamental backbone ofWSN [1–2] . WSNs have hundreds to thousands of homogeneous or heterogeneous sensors. Most WSNs their sensor nodes handle essential functions like detecting, handling, communication and computation. Their neighboring nodes’ communication is enabled through electromagnetic signals via radio frequency [3] .  \nWSNs are utilized for both tracking and monitoring purposes. WSNs can be used to monitor patient health care, provide chemicals for the rubber industry, and monitor  \ntoxic gas. WSN technologies are also found in tracking methods, such as tracking wild species, pets or people [4] . Recently, WSNs have been revised for efficient communication, innovativeness and cost-effectiveness.  \nSensor nodes (SNs) are able to decipher, identify and transmit radio frequency information [5][6] . In addition, WSNs are helpful for checking and following purposes in unavailable and hostile conditions, in which human mediation does not or cannot happen. Checking purposes include observation of concoction vapors and gaseous tension observation; tracking purposes include human tracking and animal tracking [7] . Security of computer networks has become a primary concern; the cloud has exposed network technologies to different invasive activities, leading to intense failure. Hence, it is critical to combine security instruments to ensure that the system’s security is not threatened. The purpose of a secure approach is to protect important information through determining anomalies.  \nIt is essential to fuse security components so that the security of the framework isnot threatened. The goal of a solid framework is to secure shrewd data through recognizing oddities. An Intrusion Detection System (IDS) is a powerful asset that identifies unwanted actions that can access, control, or incapacitate a PC framework, chiefly through the Internet. It screens approaching and active traffic to distinguish noxious activities that risk the security of the fra","cbCaispRmfolTuu1","https://ap.wps.com/l/cbCaispRmfolTuu1","pdf",1077315,1,10,"English","en",105,"# Introduction\n# Related works\n# Methodology\n# Feature selection\n# Experimental results\n# Conclusions","[{\"question\":\"What network problem motivates the use of SDN for security?\",\"answer\":\"Traditional networks rely on stable setups that struggle to meet the requirements of recently inserted nodes. SDN provides a more suitable structure for data-intensive applications and security handling.\"},{\"question\":\"Which learning techniques are compared in the study?\",\"answer\":\"The paper compares Deep Neural Network (DNN) with Machine Learning (ML) methods, including decision tree, Naïve Bayes, and Support Vector Machine, combined with feature selection techniques.\"},{\"question\":\"How is the proposed approach evaluated and what is the main result?\",\"answer\":\"Evaluation uses the NSL–KDD dataset (41 features, 148,517 samples) and multiple estimation measurements. The results indicate decision tree achieves the highest accuracy and effectiveness compared with other techniques and earlier studies.\"}]","A Comparative Study for SDN Security Based on Machine Learning | PDF",1785900117,25,{"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},"a-comparative-study-for-sdn-security-based-on-machine-learning","",{"@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/a-comparative-study-for-sdn-security-based-on-machine-learning/125593/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What network problem motivates the use of SDN for security?","Question",{"text":75,"@type":76},"Traditional networks rely on stable setups that struggle to meet the requirements of recently inserted nodes. SDN provides a more suitable structure for data-intensive applications and security handling.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which learning techniques are compared in the study?",{"text":80,"@type":76},"The paper compares Deep Neural Network (DNN) with Machine Learning (ML) methods, including decision tree, Naïve Bayes, and Support Vector Machine, combined with feature selection techniques.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the proposed approach evaluated and what is the main result?",{"text":84,"@type":76},"Evaluation uses the NSL–KDD dataset (41 features, 148,517 samples) and multiple estimation measurements. The results indicate decision tree achieves the highest accuracy and effectiveness compared with other techniques and earlier studies.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]