[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31673":3,"doc-seo-31673":27},{"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,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":20,"language":21,"language_code":22,"table_of_contents":23,"faqs":24,"seo_title":13,"seo_description":14,"update_tm":25,"read_time":26},31673,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?_k=1775820430993990792",6,"Technology","Implementation of Intrusion Detection System Using Various Machine Learning Approaches with Ensemble Learning","Advanced threat attacks have increased, but feature filtering–based network intrusion detection systems still struggle to help security managers and analysts identify and stop intrusions. Intrusion detection protects real and virtual computer networks by detecting malicious behavior in network traffic. Machine learning methods—including neural networks, statistical models, rule learning, and ensemble techniques—support more effective intrusion detection. This work proposes an ensemble method combining decision tree, random forest, extra tree, and XGBoost, implemented in Python and evaluated on CICIDS2017 using precision, recall, and F1-score.","cbCaitS7JK4Qj9Yq","https://ap.wps.com/l/cbCaitS7JK4Qj9Yq","pdf",461171,1,5,"English","en","# Abstract\n# Keywords\n# Introduction\n# Literature Survey","[{\"question\":\"Why do feature filtering-based network intrusion detection systems face challenges?\",\"answer\":\"They have limitations that make it difficult for security managers and analysts to identify and thwart network intrusions effectively.\"},{\"question\":\"What ensemble method is proposed for intrusion detection?\",\"answer\":\"An ensemble approach combining decision tree, random forest, extra tree, and XGBoost to improve detection accuracy.\"},{\"question\":\"How is the proposed system evaluated?\",\"answer\":\"Using the CICIDS2017 dataset and measuring precision, recall, and F1-score across evaluation criteria.\"}]",1779915620,13,{"code":4,"msg":28,"data":29},"ok",{"site_id":30,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":25},105,"implementation-of-intrusion-detection-system-using-various-machine-learning-approaches-with-ensemble-learning","",{"@graph":34,"@context":83},[35,52,66],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/technology/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/implementation-of-intrusion-detection-system-using-various-machine-learning-approaches-with-ensemble-learning/31673/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-05-27",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"Why do feature filtering-based network intrusion detection systems face challenges?","Question",{"text":73,"@type":74},"They have limitations that make it difficult for security managers and analysts to identify and thwart network intrusions effectively.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"What ensemble method is proposed for intrusion detection?",{"text":78,"@type":74},"An ensemble approach combining decision tree, random forest, extra tree, and XGBoost to improve detection accuracy.",{"name":80,"@type":71,"acceptedAnswer":81},"How is the proposed system evaluated?",{"text":82,"@type":74},"Using the CICIDS2017 dataset and measuring precision, recall, and F1-score across evaluation criteria.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":30}]