[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290372-105":59,"doc-detail-290372-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","anomaly-detection-in-network-traffic-using-machine-learning","Anomaly Detection in Network Traffic Using Machine Learning","","Rapid growth in internet-connected devices makes network security increasingly challenging, resulting in larger volumes of traffic data that are exposed to phishing, denial-of-service attacks, malware, and unauthorized access. Accurate monitoring depends on reliable anomaly detection to prevent intrusions. This paper proposes an ML-based anomaly detection approach and evaluates five algorithms—AdaBoost, KNN, Random Forest, MLP, and ID3—on the CICIDS2017 benchmark. Performance is measured using accuracy, precision, recall, and f-measure, showing KNN with 97% accuracy and ID3 offering a balanced trade-off.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/anomaly-detection-in-network-traffic-using-machine-learning/290372/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/anomaly-detection-in-network-traffic-using-machine-learning/290372.png","ImageObject",300,407,{"name":92,"@type":93},"Eliana","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":46},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is anomaly detection important for network security?","Question",{"text":112,"@type":113},"It detects abnormal activity across network data, supporting intelligent systems that learn from traffic patterns and reducing risks from undetected anomalies.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which dataset and algorithms are evaluated in the study?",{"text":117,"@type":113},"The study uses the CICIDS2017 dataset and evaluates AdaBoost, K-Nearest Neighbor (KNN), Decision Trees (ID3), Random Forest (RF), and Multi-Layer Perceptron (MLP).",{"name":119,"@type":110,"acceptedAnswer":120},"How is algorithm performance measured, and what are the key results?",{"text":121,"@type":113},"Performance is assessed using accuracy, precision, recall, and f-measure. KNN achieves the highest accuracy at 97%, while ID3 provides a balanced performance-efficiency trade-off.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290372,1789641516,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":46,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267","INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION  \n[journal homepage :](journal homepage : www.joiv.org/index.php/joiv)[ www.joiv.org/index.php/joiv](journal homepage : www.joiv.org/index.php/joiv)  \nAnomaly Detection in Network Traffic Using Machine Learning Amir Muhammad Hafiz Othmana,b, Mohd Faizal Ab Razaka,1, M. Izham Jayaa, Nurul Azma Abdullah c,2,  \nAlde Alandad  \na Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Pahang, Malaysia b Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia c Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia d Department of Information Technology, Politeknik Negeri Padang, Padang, Indonesia  \n[Corresponding author:](Corresponding author:1 azma@uthm.edu.my)[1](Corresponding author:1 azma@uthm.edu.my)[ azma@uthm.edu.my](Corresponding author:1 azma@uthm.edu.my); [2](2faizalrazak@umpsa.edu.my)[faizalrazak@umpsa.edu.my](2faizalrazak@umpsa.edu.my)  \nAbstract—Ensuring network security has become difficult due to the rapid growth of internet-connected devices, leading to a significant increase in network traffic data. The enormous amount of data generated is vulnerable to multiple types of cyberattacks and intrusions, including phishing, denial-of-service (DoS) attacks, malware, and unauthorized access. Therefore, it is critical to ensure that the network traffic is properly monitored and secured by accurately detecting anomalies to prevent intrusions. One effective method to achieve this is to implement machine learning techniques, which have gained attention among researchers for analyzing complex patterns in datasets. In this paper, an anomaly detection method is proposed using machine learning (ML) techniques. The study objective is to analyze the effectiveness and reliability of implementing machine learning techniques in identifying anomalies in network traffic. Five (5) algorithms, which are Adaptive Boosting (AdaBoost), K-Nearest Neighbor (KNN), Random Forest (RF), Multi-Layer Perceptron (MLP), and Decision Trees (ID3) are systematically evaluated using the dataset CICIDS2017, a comprehensive and widely adopted benchmark for network traffic detection research. Five (5) performance metrics, which are accuracy, precision, recall, and fmeasure, are utilized to assess the result of each algorithm. The results highlight that the KNN algorithm achieves the highest accuracy, at 97%, while the ID3 algorithm produces a balanced trade-off between performance and efficiency. These findings demonstrate that machine learning techniques have strong potential to enhance network security by improving anomaly detection, providing a promising direction for developing intelligent, adaptive intrusion detection.  \nKeywords—Anomaly detection; machine learning; network security; CICIDS2017; classification algorithms.  \nManuscript received 7 Aug. 2025; revised 16 Oct. 2025; accepted 21 Jan. 2026. Date of publication 31 Mar. 2026.  \nInternational Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.  \nI. INTRODUCTION  \nNetwork traffic has become a necessary aspect of our daily life as the internet grows rapidly and the number of connected devices. It is used in numerous services such as online payment and streaming video. However, as the volume of data transmitted over the network increases, maintaining the network’s security and reliability has become more difficult, leading to an increase in network security attacks [1], [2], [3] .  \nAnomaly detection is a crucial mechanism for network security, as it can detect abnormal activity across a variety of network data. It supports the development of an intelligent system capable of learning autonomously from data [4] . There are two types of network data that can be used for anomaly detection: network traffic and user activity logs [5] . Machine ","cbCaib4OwFfGyXVG","https://ap.wps.com/l/cbCaib4OwFfGyXVG","pdf",3493770,"English","# Abstract\n# Keywords\n# Introduction\n# Materials and Methods\n## Dataset Collection","[{\"question\":\"Why is anomaly detection important for network security?\",\"answer\":\"It detects abnormal activity across network data, supporting intelligent systems that learn from traffic patterns and reducing risks from undetected anomalies.\"},{\"question\":\"Which dataset and algorithms are evaluated in the study?\",\"answer\":\"The study uses the CICIDS2017 dataset and evaluates AdaBoost, K-Nearest Neighbor (KNN), Decision Trees (ID3), Random Forest (RF), and Multi-Layer Perceptron (MLP).\"},{\"question\":\"How is algorithm performance measured, and what are the key results?\",\"answer\":\"Performance is assessed using accuracy, precision, recall, and f-measure. KNN achieves the highest accuracy at 97%, while ID3 provides a balanced performance-efficiency trade-off.\"}]","Anomaly Detection in Network Traffic Using Machine Learning | PDF",18]