[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125104-en":3,"doc-seo-125104-105":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},125104,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","THE UTILIZATION OF MACHINE LEARNING FOR NETWORK INTRUSION DETECTION SYSTEMS","This study investigates integrating a Multilayer Perceptron (MLP) architecture into Network Intrusion Detection Systems (NIDS) to strengthen defenses against evolving cyber threats. The research examines how MLP learns complex patterns and adapts to dynamic attack vectors, aiming to improve detection accuracy. Results from 5-fold cross-validation show consistent performance with average accuracy of 0.97 and low standard deviation, while additional tests across nodes and train-test splits confirm robustness via high AUC-ROC and F1-Score metrics, alongside identified challenges such as limited labelled datasets and interpretability.","[http://doi.org/10.35784/iapgos.6388](http://doi.org/10.35784/iapgos.6388 received:)[ received:](http://doi.org/10.35784/iapgos.6388 received:) 11.06.2024 | revised: 05.11.2024 | accepted: 05.11.2024 | available online: 21.12.2024  \nTHE UTILIZATION OF MACHINE LEARNING FOR NETWORK INTRUSION DETECTION SYSTEMS  \nAhmad Sanmorino1, Herri Setiawan2, John Roni Coyanda1  \n1Universitas Indo Global Mandiri, Department of Information Systems, Palembang, Indonesia, 2Universitas Indo Global Mandiri, Department of Informatics Engineering, Palembang, Indonesia  \nAbstract. This study investigates the integration of Multilayer Perceptron (MLP) architecture in Network Intrusion Detection Systems (NIDS) to strengthen cyber defences against evolving threats. The goal is to explore the potential of MLP in learning complex patterns and adapting to dynamic attack vectors, thereby improving detection accuracy. Key results from 5-fold cross-validation demonstrate model consistency, achieving an average accuracy of 0.97 with minimal standard deviation. Further evaluation across multiple nodes per layer and train-test splits demonstrate model robustness, displaying high metrics such as AUC-ROC and F1-Score. Challenges, such as the scarcity of large labelled datasets and complex model interpretability, are acknowledged. This study provides a comprehensive foundation for future investigations, suggesting potential directions such as integrating advanced neural network architectures and assessing model transferability. In conclusion, this study contributes to the evolving intersection of machine learning and cyber security, offering insights into the strengths, limitations, and future directions of MLP-based NIDS. As cyber threats evolve, continued refinement of MLP methods is critical to effective network defences against sophisticated adversaries.  \nKeywords: network intrusion, multilayer perceptrons, machine learning  \nWYKORZYSTANIE UCZENIA MASZYNOWEGO  \nW SYSTEMACH WYKRYWANIA WŁAMANIA DO SIECI  \nStreszczenie. W niniejszym artykule zbadano integrację architektury wielowarstwowego perceptronu (MLP) w systemach wykrywania włamań do sieci (NIDS) w celu wzmocnienia cyberobrony przed ewoluującymi zagrożeniami. Celem jest zbadanie potencjału MLP w uczeniu się złożonych wzorcówi dostosowywaniu się do dynamicznych wektorów ataków, a tym samym poprawienie dokładności wykrywania. Kluczowe wyniki 5-krotnej walidacji krzyżowej wykazują spójność modelu, osiągając średnią dokładność 0,97 przy minimalnym odchyleniu standardowym. Dalsza ocena w wielu węzłach na warstwę ipodziały trening-test wykazują solidność modelu, wykazując wysokie metryki, takiejakAUC-ROC i F1-Score. Wyzwania, takie jak niedobórdużych zestawów danych z etykietami i złożona interpretowalność modelu, są uznawane. Niniejsze badanie zapewnia kompleksową podstawę do przyszłych badań, sugerując potencjalne kierunki, takie jak integracja zaawansowanych architektur sieci neuronowych i ocena przenoszalności modelu. Podsumowując, niniejsze badanie przyczynia się do ewoluującego skrzyżowania uczenia maszynowego i cyberbezpieczeństwa, oferując wgląd w mocnestrony, ograniczenia i przyszłe kierunki NIDS opartych na MLP. W miarę rozwoju cyberzagrożeń ciągłe udoskonalanie metod MLP staje się kluczowedla skutecznej obrony sieci przed wyrafinowanymi przeciwnikami.  \nSłowa kluczowe: włamania do sieci, perceptrony wielowarstwowe, uczenie maszynowe Introduction  \nIn the landscape of cyber security, the sophistication of cyber threats provides innovative and adaptive solutions to strengthen network defences. Network Intrusion Detection Systems (NIDS) are at the forefront of these defences, serving as a critical component in identifying and thwarting malicious activity in computer networks [3, 15] . In recent years, there has been a major shift towards integrating machine learning (ML) techniques to improve the effectiveness of intrusion detection. This study investigates the advancements in ML, specifically focusing on","cbCaico1mTJMPd7t","https://ap.wps.com/l/cbCaico1mTJMPd7t","pdf",748998,1,4,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Machine learning shift in intrusion detection\n## Motivation for MLP-based NIDS\n## Ongoing challenges","[{\"question\":\"What is the main objective of the study on MLP-based NIDS?\",\"answer\":\"The study aims to integrate an MLP architecture into NIDS to learn complex patterns and adapt to evolving attack vectors, improving detection accuracy.\"},{\"question\":\"How is model performance evaluated in the study?\",\"answer\":\"Performance is evaluated using 5-fold cross-validation, and further assessed across multiple nodes per layer and different train-test splits using metrics such as AUC-ROC and F1-Score.\"},{\"question\":\"What key challenges are acknowledged when using MLP for intrusion detection?\",\"answer\":\"The study highlights the scarcity of large labelled datasets for training and the difficulty of interpreting complex models.\"}]","THE UTILIZATION OF MACHINE LEARNING FOR NETWORK INTRUSION DETECTION SYSTEMS | PDF",1785896667,10,{"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},"the-utilization-of-machine-learning-for-network-intrusion-detection-systems","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/the-utilization-of-machine-learning-for-network-intrusion-detection-systems/125104/",{"url":52,"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","2026-08-05",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 is the main objective of the study on MLP-based NIDS?","Question",{"text":74,"@type":75},"The study aims to integrate an MLP architecture into NIDS to learn complex patterns and adapt to evolving attack vectors, improving detection accuracy.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How is model performance evaluated in the study?",{"text":79,"@type":75},"Performance is evaluated using 5-fold cross-validation, and further assessed across multiple nodes per layer and different train-test splits using metrics such as AUC-ROC and F1-Score.",{"name":81,"@type":72,"acceptedAnswer":82},"What key challenges are acknowledged when using MLP for intrusion detection?",{"text":83,"@type":75},"The study highlights the scarcity of large labelled datasets for training and the difficulty of interpreting complex models.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]