[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-12889":3,"doc-seo-12889":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":19,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"table_of_contents":24,"faqs":24,"seo_title":13,"seo_description":14,"update_tm":25,"read_time":26},12889,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?_k=1775820430993990792",8,"Research & Report","Intrusion Detection With Deep Learning Classifiers: A Synergistic Approach of Probabilistic Clustering and Human Expertise to Reduce False Alarms","Deep learning–based intrusion detection often faces high false alarm rates that hinder deployment in critical networks. The proposed human-machine framework reduces false alarms by applying probabilistic clustering to regroup traffic by model-derived probabilities, identifying high false-alarm clusters as uncertain, and routing their traffic to human experts for final decisions. A next-generation firewall supports efficient expert handling. Experiments with CNN and hybrid RNN on CICDDoS2019, UNSW-NB15, and CICIDS2017 show false positives and false negatives reduced by up to 79.61% and 86.99%.","cbCaiqJcc8u7zpo0","https://ap.wps.com/l/cbCaiqJcc8u7zpo0","pdf",6430856,14,1,23,"English","en","",1776448832,58,{"code":4,"msg":28,"data":29},"ok",{"site_id":30,"language":23,"slug":31,"title":13,"keywords":24,"description":14,"schema_data":32,"social_meta":67,"head_meta":70,"extra_data":72,"updated_unix":25},105,"intrusion-detection-with-deep-learning-classifiers-a-synergistic-approach-of-probabilistic-clustering-and-human-expertise-to-reduce-false-alarms",{"@graph":33,"@context":66},[34,51],{"@type":35,"itemListElement":36},"BreadcrumbList",[37,41,45,48],{"item":38,"name":39,"@type":40,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":42,"name":43,"@type":40,"position":44},"https://docshare.wps.com/document/","Document",2,{"item":46,"name":12,"@type":40,"position":47},"https://docshare.wps.com/document/research-report/",3,{"item":49,"name":13,"@type":40,"position":50},"https://docshare.wps.com/document/intrusion-detection-with-deep-learning-classifiers-a-synergistic-approach-of-probabilistic-clustering-and-human-expertise-to-reduce-false-alarms/12889/",4,{"url":49,"name":13,"@type":52,"author":53,"headline":13,"publisher":55,"fileFormat":58,"description":14,"dateModified":59,"datePublished":60,"encodingFormat":58,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":54},"Person",{"url":38,"name":56,"@type":57},"DocShare","Organization","application/pdf","2026-06-05","2026-04-17",true,{"@type":63,"interactionType":64,"userInteractionCount":19},"InteractionCounter",{"@type":65},"ViewAction","https://schema.org",{"og:url":68,"og:type":69,"og:title":13,"og:site_name":56,"og:description":14},"https://docshare.wps.com/document/intrusion-detection-with-deep-learning-classifiers-a-synergistic-approach-of-probabilistic-clustering-and-human-expertise-to-reduce-false-alarms/12889","article",{"robots":71,"canonical":68},"index,follow",{"doc_id":7,"site_id":30}]