[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125215-en":3,"doc-seo-125215-105":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},125215,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","ARCHITECTURAL FRAMEWORK OF A PROTOTYPE FOR ANOMALY DETECTION IN NETWORK TRAFFIC USING MACHINE LEARNING","This paper presents a prototype application for detecting network traffic anomalies by combining visual analytics with unsupervised machine learning. The system is implemented with a Flask-based three-tier architecture and uses the Isolation Forest algorithm for anomaly detection. It includes interactive web visualizations to improve interpretability of complex traffic patterns, such as temporal flow charts, protocol distribution analysis, and anomaly severity classification. The prototype helps administrators identify sophisticated intrusions via real-time metrics and supports decision-making for threat mitigation.","# ARCHITECTURAL FRAMEWORK OFA PROTOTYPE FOR ANOMALYDETECTION IN NETWORK TRAFFIC USING MACHINE LEARNING\n\n## X.Wang,A.Prudnik\n\nEducational Institution “Belarusicm State University of Informatics and Radioelectronics”,Minsk,Belarus  \nAbstract.This paper presents a prototype application for detecting network traffic anomalies by integratingvisual analytics and unsupervised machine learning.Built using a Flask-based three-tier architecture.the systememploys the Isolation Forest algorithm for anomaly detection and provides interactive web-based visualizationsto enhance human interpretation of complex traffic patterns Key features include temporal traffic flowvisualization.protocol distribution analysis and anomaly severity classification The prototype enables networkadministrators to identify sophisticated intnusions through real-time metrics and supports informed decision-making for threat mitigation.  \nKeywords:network traffic;anomaly detection;visual analytics:machine learning;web-based dashboard.  \n## Introduction\n\nNetwork security faces increasing challenges from sophisticated cyber attacks amidstexponential traffic growth.Traditional signature-based detection systems struggle to identifynovel threats,necessitating advanced approaches like anomaly detection [1].While machinelearning offers enhanced detection capabilities,unsupervised methods often produce highfalse positives,and purely algorithmic systems lack interpretability for securitypractitioners [2,3].This research introduces a hybrid framework that combines unsupervisedmachine learning with visual analytics to address scalability and interpretability challenges.By leveraging the isolation forest algorithm and interactive visualizations,the prototype aimsto empower network administrators to detect and contextualize anomalies effectively  \n## Main Part\n\nThe theoretical foundation of this prototype rests on integrating automated anomalydetection with human-centered visual analytics.Unsupervised machine learning,specificallythe isolation forest algorithm,is chosen for its ability to identify statistical outliers in high-dimensional data without requiring labeled datasets [4].This method constructs randombinary trees to isolate anomalies,where outliers have shorter path lengths due to their distinctfeatures.  Complementing this,visual analytics facilitates human interpretationby transforming complex data into intuitive graphical representations,enabling analyststo contextualize anomalies within operational environments [5].The theoretical designemphasizes a synergy between machine-driven detection and human-driven analysisto enhance overall system effectiveness in identifying network threats.  \nThe prototype adopts a three-tier architecture comprising data acquisition,analysis,and presentation layers(Fig.1).The data acquisition layer generates synthetic network trafficusing a custom data generator module,producing records with attributes like timestamp,source/destination IPs,protocol,and byte counts for testing purposes.The analysis layeremploys the isolation forest algorithm for anomaly detection,with data persistence handledby SQLite and SQLAlchemy ORM.The presentation layer uses a Flask web framework witha Bootstrap-based interface,rendering visualizations via Chart.js for temporal traffic,protocoldistribution,and anomaly severity metrics.  \nFig.1.Three-tier architecture of the network anomaly detection prototype  \nComponents communicate through RESTful API endpoints,ensuring modularityand separation of concerns.Data flows sequentially:traffic records are generated,stored,analyzed in hourly batches,and visualized on-demand via API triggers.This design supportsscalability and iterative development while maintaining computational efficiency  \nThe visual analytics framework transforms network traffic data into interactivevisualizations using Chart.js,enhancing human interpretation for network administrators.Temporal traffic analysis is presented through line charts that disp","cbCaiagW0Rh6K4GW","https://ap.wps.com/l/cbCaiagW0Rh6K4GW","pdf",432910,1,4,"English","en",105,"# Introduction\n# Main Part\n## Prototype Design and Architecture\n## Visual Analytics and Interpretation\n## Anomaly Detection Module and Performance","[{\"question\":\"Why does the research use anomaly detection instead of signature-based methods?\",\"answer\":\"Traditional signature-based systems struggle with novel threats as traffic grows. Anomaly detection can flag unusual patterns that may not match known signatures.\"},{\"question\":\"How does the prototype detect anomalies?\",\"answer\":\"It uses the Isolation Forest algorithm to identify statistical outliers in high-dimensional traffic data without needing labeled datasets.\"},{\"question\":\"What visualization features are provided to help analysts interpret results?\",\"answer\":\"The prototype offers temporal traffic flow charts, protocol distribution doughnut charts, and anomaly severity pie charts with severity thresholds mapped to color-coded levels.\"}]","ARCHITECTURAL FRAMEWORK OF A PROTOTYPE FOR ANOMALY DETECTION IN NETWORK TRAFFIC USING MACHINE LEARNING | PDF",1785897522,10,{"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},"architectural-framework-of-a-prototype-for-anomaly-detection-in-network-traffic-using-machine-learning","",{"@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/architectural-framework-of-a-prototype-for-anomaly-detection-in-network-traffic-using-machine-learning/125215/",{"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},"Why does the research use anomaly detection instead of signature-based methods?","Question",{"text":74,"@type":75},"Traditional signature-based systems struggle with novel threats as traffic grows. Anomaly detection can flag unusual patterns that may not match known signatures.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the prototype detect anomalies?",{"text":79,"@type":75},"It uses the Isolation Forest algorithm to identify statistical outliers in high-dimensional traffic data without needing labeled datasets.",{"name":81,"@type":72,"acceptedAnswer":82},"What visualization features are provided to help analysts interpret results?",{"text":83,"@type":75},"The prototype offers temporal traffic flow charts, protocol distribution doughnut charts, and anomaly severity pie charts with severity thresholds mapped to color-coded levels.","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"]