[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31622":3,"doc-seo-31622":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},31622,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Threat Detection and Classification Using ML Techniques","Research focuses on detecting intrusions from unwanted sources such as spam, advertisements, and fake sites that can harm users. Machine learning models are trained using the CICIDS2017 dataset, comparing approaches including Random Forest and Support Vector Machine (SVM) to identify the best trade-off between time efficiency and accuracy. The system predicts whether network traffic is dangerous or not, enabling real-time threat classification suitable for current conditions.","cbCaifUWHxkF9Shi","https://ap.wps.com/l/cbCaifUWHxkF9Shi","pdf",427532,1,7,"English","en","# Introduction\n## Target selection by Hackers\n## Recognizing you are a Target\n## Using AI and ML to Shield self","[{\"question\":\"What kinds of intrusions does the project aim to detect?\",\"answer\":\"The study targets intrusions from unwanted sources such as spam, ads, and fake sites that may compromise users.\"},{\"question\":\"Which dataset and models are used for training and evaluation?\",\"answer\":\"Training uses the CICIDS2017 dataset, and models such as Random Forest and SVM are evaluated to find the best results with less time and higher accuracy.\"},{\"question\":\"How does the proposed system help users in practice?\",\"answer\":\"By predicting whether traffic is dangerous or not and warning about suspicious or fake URLs using machine learning, it supports timely threat detection and reduces risk from phishing and similar attacks.\"}]",1779829243,18,{"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,"threat-detection-and-classification-using-ml-techniques","",{"@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/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/threat-detection-and-classification-using-ml-techniques/31622/",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","2026-05-26",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},"What kinds of intrusions does the project aim to detect?","Question",{"text":73,"@type":74},"The study targets intrusions from unwanted sources such as spam, ads, and fake sites that may compromise users.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Which dataset and models are used for training and evaluation?",{"text":78,"@type":74},"Training uses the CICIDS2017 dataset, and models such as Random Forest and SVM are evaluated to find the best results with less time and higher accuracy.",{"name":80,"@type":71,"acceptedAnswer":81},"How does the proposed system help users in practice?",{"text":82,"@type":74},"By predicting whether traffic is dangerous or not and warning about suspicious or fake URLs using machine learning, it supports timely threat detection and reduces risk from phishing and similar attacks.","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}]