[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127339-en":3,"doc-seo-127339-105":30,"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":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},127339,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense","In the dynamic world of cybersecurity, strong solutions are essential to protect complex network systems. This study examines how machine learning can strengthen digital infrastructure security by improving network anomaly detection and security protection. It evaluates ensemble approaches and supervised learning for identifying anomalies and reducing risks, with emphasis on real-time monitoring and adaptive responses. Models are applied to a real-world dataset and assessed using single-valued neutrosophic numbers (SVNNs) and multi-criteria decision-making with EDAS to rank performance.","Neutrosophic Sets and Systems, Vol. 83, 2025  \nUniversity of New Mexico   \nMachine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense Technology  \nHussein S Al-Khazraji1, Ahmed M. Alkhamees2, Humam M Al-Doori3, Ahmed A. Metwaly4, Mohamed eassa5,6, Ahmed  \nAbdelhafeez5,6, Ahmed S. Salama7, Ahmad M. Nagm7  \n1Department of Electrical Power Engineering Technologies, Al-Hussein University College, Karbala, Iraq  \n[hussain.safaa@huciraq.edu.iq](hussain.safaa@huciraq.edu.iq)  \n2College of Health and Medical Technologies / Department of Anesthesia Technologies, Ahl Al Bayt University  \nKarbala, Iraq, Iraq, [Ahmedmon89@abu.edu.iq](Ahmedmon89@abu.edu.iq)  \n3Department of Computer Engineering Techniques, Al-Yarmok University College Diyala, Iraq,  \n4Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt, [a.metwaly23@fci.zu.edu.eg](a.metwaly23@fci.zu.edu.eg)  \n5Computer Science Department, Faculty of Information Systems and Computer Science, October 6 University, Giza, 12585, [Egypt mohamed.eassa.cs@o6u.edu.eg](Egypt mohamed.eassa.cs@o6u.edu.eg); [aahafeez.scis@o6u.edu.eg](aahafeez.scis@o6u.edu.eg)  \n6Applied Science Research Center. Applied Science Private University, Amman, Jordan  \n7Department of Computer Engineering and Electronics, Cairo Higher Institute for Engineering, Computer Science and Management, New Cairo, Egypt [ahmadnagm@alazhar.edu.eg](ahmadnagm@alazhar.edu.eg), [A.salama@chi.edu.eg](A.salama@chi.edu.eg)  \nAbstract: In the dynamic world of cybersecurity, strong solutions are necessary to safeguard intricate network systems. By looking at network anomaly detection and security protection, this study investigates how machine learning (ML) might increase digital infrastructure security. We assess how well critical ML approaches, such as ensemble approaches and supervised learning, identify anomalies and lessen risks. The examination of ML-based systems integration into comprehensive security frameworks places a strong emphasis on real-time monitoring and adaptive responses. Examples from real-world situations highlight how crucial ML is to improving network security. After, we apply different ML models to the real-world dataset. Then we use the single-valued Neutrosophic numbers (SVNNs) methodology to evaluate these ML models and select the best one. We use the multi-criteria decision-making (MCDM) approach to obtain the criteria weights and rank the ML models using the EDAS method. The results show that the random forest model is the best ML model under different evaluation matrices.  \nKeywords: Neutrosophic Number; Security; Network Anomaly Detection; Cybersecurity; Uncertainty.  \n1. Introduction  \nNetwork security is a crucial concern in today's technologically advanced society because of the ever-changing cyber threats. Which pose serious difficulties for both people and organizations. [1] . The interconnectedness of digital ecological systems, which include personal gadgets and critical infrastructure, exacerbates the impact of intrusions. The main topic of this paper is the complexity and urgency of network security, which emphasizes the use of machine learning (ML) in identifying network abnormalities and defending against them. [2] .  \nMachine learning (ML) has been shown to be a powerful tool in cybersecurity, successfully managing the complexity and dynamic nature of cyber threats. Through trend identification and prediction analysis, machine learning (ML) enables initiative-taking threat detection and realtime monitoring, in contrast to traditional security methods. Because of its ability to continually adapt from fresh data, it efficiently counters sophisticated attacks that often evade traditional defenses. [3] .  \nThe use of machine learning (ML) in complete safety measures improves overall defensive capabilities, lowering the demand for human interaction while increasing reaction speed and effectiveness. ML allows","cbCaiiagcjuWSzBV","https://ap.wps.com/l/cbCaiiagcjuWSzBV","pdf",816690,1,15,"English","en",105,"# 1. Introduction\n## Network security challenges\n## Role of machine learning in cybersecurity\n## Need for proactive and adaptive defenses\n## Limitations of conventional security methods","[{\"question\":\"How does the study use machine learning for network security?\",\"answer\":\"It applies machine learning models to network anomaly detection to identify abnormal behavior and reduce security risks, supporting real-time monitoring and adaptive responses.\"},{\"question\":\"Which methods are used to evaluate and compare the machine learning models?\",\"answer\":\"The models are evaluated with single-valued neutrosophic numbers (SVNNs). Multi-criteria decision-making is used to obtain criteria weights and rank models using the EDAS method.\"},{\"question\":\"What is the best-performing model according to the results?\",\"answer\":\"The random forest model is reported as the best model under different evaluation matrices.\"}]","Machine Learning Models with Neutrosophic Numbers for Network Anomaly Detection and Security Defense | PDF",1785938372,38,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-models-with-neutrosophic-numbers-for-network-anomaly-detection-and-security-defense","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-models-with-neutrosophic-numbers-for-network-anomaly-detection-and-security-defense/127339/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",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 use machine learning for network security?","Question",{"text":76,"@type":77},"It applies machine learning models to network anomaly detection to identify abnormal behavior and reduce security risks, supporting real-time monitoring and adaptive responses.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which methods are used to evaluate and compare the machine learning models?",{"text":81,"@type":77},"The models are evaluated with single-valued neutrosophic numbers (SVNNs). Multi-criteria decision-making is used to obtain criteria weights and rank models using the EDAS method.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the best-performing model according to the results?",{"text":85,"@type":77},"The random forest model is reported as the best model under different evaluation matrices.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]