[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291187-105":59,"doc-detail-291187-en":131},{"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":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","machine-learning-to-improve-the-performance-of-anomalybased-network-intrusion-detection-in-big-data","Machine learning to improve the performance of anomalybased network intrusion detection in big data","","Machine learning is applied to strengthen anomaly-based network intrusion detection in big-data conditions where cyber-attacks evolve and network traffic becomes difficult to analyze. The work builds an IDS classification pipeline on the CSE-CIC-IDS-2018 dataset using preprocessing with under-sampling, feature selection, and multiple machine-learning classifiers. Experiments compare seven algorithms to identify the best-performing model, showing the MLP approach achieves the strongest evaluation results while addressing challenges such as imbalance, high dimensionality, and large traffic volume.",{"@graph":69,"@context":123},[70,84,106],{"@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/machine-learning-to-improve-the-performance-of-anomalybased-network-intrusion-detection-in-big-data/291187/",{"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/machine-learning-to-improve-the-performance-of-anomalybased-network-intrusion-detection-in-big-data/291187.png","ImageObject",300,407,{"name":92,"@type":93},"Aurelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is intrusion detection challenging in the big data era?","Question",{"text":113,"@type":114},"Big data network traffic is vast, complex, and continuously changing, making it harder to distinguish normal communications from evolving attacks while also coping with issues like high false alarms and imbalanced data.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What dataset and preprocessing approach are used in the study?",{"text":118,"@type":114},"The study evaluates models on the CSE-CIC-IDS-2018 dataset and uses data preprocessing techniques including under-sampling and feature selection to improve classification performance.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning classifier achieved the best results?",{"text":122,"@type":114},"Experimental results indicate the MLP (multi-layer perceptron) algorithm delivered the most successful performance with the best evaluation metrics.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291187,1789643626,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},1099514068365,"https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1794309523707217728","Machine learning to improve the performance of anomalybased network intrusion detection in big data  \nSiriporn Chimphlee, Witcha Chimphlee  \nDepartment of Data Science and Analytics, Faculty of Science and Technology, Suan Dusit University, Bangkok, Thailand  \nArticle history:  \nReceived Nov 14, 2022 Revised Jan 5, 2023 Accepted Jan 10, 2023  \nKeywords:  \nClass imbalance CSE-CIC-IDS-2018  \nFeature selection Machine learning Network intrusion detection  \nCorresponding Author:  \nWith the rapid growth of digital technology communications are overwhelmed by network data traffic. The demand for the internet is growing every day in today's cyber world, raising concerns about network security. Big Data are a term that describes a vast volume of complicated data that is critical for evaluating network patterns and determining what has occurred in the network. Therefore, detecting attacks in a large network is challenging. Intrusion detection system (IDS) is a promising cybersecurity research field. In this paper, we proposed an efficient classification scheme for IDS, which is divided into two procedures, on the CSE-CIC-IDS-2018 dataset, data pre-processing techniques including under-sampling, featureselection, and classifier algorithms were used to assess and decide the best performing model to classify invaders. We have implemented and compared seven classifier machine learning algorithms with various criteria. This work explored the application of the random forest (RF) for feature selection in conjunction with machine learning (ML) techniques including linear regression (LR), k-Nearest Neighbor (k-NN), classification and regression trees (CART), Bayes, RF, multi layer perceptron (MLP), and XGBoost in order to implement IDSS. The experimental results show that the MLP algorithm in the most successful with best performance with evaluation matrix.  \nThis is an open access article under the CC BY-SA license.  \nWitcha Chimphlee  \nDepartment of Data Science and Analytics, Faculty of Science and Technology, Suan Dusit University 295 Nakornratchasrima Road, Dusit, Bangkok, Thailand  \n[Email: witcha_chi@dusit.ac.th](Email: witcha_chi@dusit.ac.th), [witcha.chi@gmail.com](witcha.chi@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nInformation and communication technology (ICT) now play a critical role in all aspects of business and people's lives. At the same time, in the big data era, cyber-attacks against ICT are growing more complicated and are rapidly expanding [1] . As a result, network attacks are the most pressing issue in modern society. Malicious threats, on the other hand, are always emerging and evolving, necessitating a sophisticated security solution for the network. The number of networked computers has been growing because of widespread Internet use. Data science techniques have been applied in recent years to construct effective intrusion detection systems (IDSs) that can discern between legitimate and hijacked communications IDSs have been widely used to address for monitoring and detecting malicious actions in communications networks. IDSs can be divided into three categories: Systems for detecting intrusions that use signatures, anomaly-based systems, and hybrid systems. Anomaly-based can identify unknown harmful actions by identifying any deviations from a model built based on typical behavior, whereas signature-based can identify known assaults with corresponding collected signatures, however it has a high false alarm rate [2]–[5]. Current anomaly intrusion detection methods are suffering from accurate performance. Some of datasets suffer from providing diversity and volume of  \nnetwork traffic, some do not contain different or latest attack patterns, while others lack feature set metadata information [6] . The hybrid IDS obtained combining anomaly-based and misuse-based IDSs and shows that the hybrid IDS is a more powerful system.  \nThe idea of intrusion detection in a system is the recognition of an effort to ent","cbCaihvzkVcvG7y0","https://ap.wps.com/l/cbCaihvzkVcvG7y0","pdf",686873,14,"English","# ABSTRACT\n# 1. INTRODUCTION","[{\"question\":\"Why is intrusion detection challenging in the big data era?\",\"answer\":\"Big data network traffic is vast, complex, and continuously changing, making it harder to distinguish normal communications from evolving attacks while also coping with issues like high false alarms and imbalanced data.\"},{\"question\":\"What dataset and preprocessing approach are used in the study?\",\"answer\":\"The study evaluates models on the CSE-CIC-IDS-2018 dataset and uses data preprocessing techniques including under-sampling and feature selection to improve classification performance.\"},{\"question\":\"Which machine learning classifier achieved the best results?\",\"answer\":\"Experimental results indicate the MLP (multi-layer perceptron) algorithm delivered the most successful performance with the best evaluation metrics.\"}]","Machine learning to improve the performance of anomalybased network intrusion detection in big data | PDF",35]