[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118481-en":3,"doc-seo-118481-105":30,"detail-sidebar-cat-0-en-105":91},{"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},118481,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A Comprehensive Survey On Machine Learning-Based Intrusion Detection System for Vehicular Area Network Architectures - Comprehensive Survey","Advances in communication technologies have intensified the importance of securing VANET (Vehicular Area Network) environments. As usage expands, detecting attacks on VANET networks has become a critical requirement, leading to multiple IDS solutions, particularly those leveraging machine learning. This article reviews recent literature on machine learning-based VANET intrusion detection systems by presenting VANET architecture and security requirements, summarizing related studies, and comparing methods across key parameters. Results show that numerous ML models achieve strong detection performance, and that beyond the chosen algorithms, dataset, simulation tools, and evaluation criteria strongly influence outcomes. The work guides future research through these literature-based comparisons.","Düzce University  \nJournal of Science & Technology  \nResearch Article  \nA Comprehensive Survey On Machine Learning-Based Intrusion Detection System for Vehicular Area Network Architectures  \n Deniz BALTAa*,  Ünal ÇAVUŞOĞLU a,  Musa BALTAb  \na Yazılım Mühendisliği, Bilgisayar ve Bilişim Bilimleri Fakültesi, Sakarya Üniversitesi, Sakarya, TÜRKİYE b BilgisayarMühendisliği, Bilgisayar ve Bilişim Bilimleri Fakültesi, Sakarya Üniversitesi, Sakarya, TÜRKİYE  \n* Sorumlu yazarı[n e-posta adresi: ddural@sakarya.edu.tr](n e-posta adresi: ddural@sakarya.edu.tr)  \nDOI: 10.29130/dubited.1372131  \nABSTRACT  \nToday, the development of communication technologies causes changes in many different areas. One of these areas is VANET (Vehicular Area Network) application area. With the increase in usage areas in the VANET field, ensuring VANET network security has become more critical. Many different systems have been developed to detect attacks on VANET networks. Machine learning-based systems are one of the most widely used methods in developing these intrusion detection systems (IDS) . In this article, research on machine learning-based VANETIDS, which has been done recently in the literature, has been carried out. First, VANET architecture and security requirements are presented, then a comprehensive literature summary is given, and comparisons are made on different parameters. As a result, it has been determined that many different machine learning models are used in IDSs and perform high-performance detection. In addition to the machine learning algorithm used in the performance ofIDSs, it has been shown that many different parameters play a critical role in the performance. The paper aims to guide new studies in this field with the gains that will increase the performance of intrusion detection systems because of the literature comparison (considering criteria such as machine learning model, simulation tools, dataset, machine learning algorithm, and performance criteria) .  \nKeywords: IDS, Machine learning, Security, Vehicular networks  \nAraçsal Ağ Mimarisi İçin Makine Öğrenmesi Tabanlı Saldırı Tespit Sistemi Üzerine Kapsamlı Bir Araştırma  \nÖz  \nGünümüzde iletişim teknolojilerinin gelişmesi birçok farklı alanda değişimlere neden olmaktadır. Bu alanlardan biri de VANET (Araç Alan Ağı) uygulama alanıdır. VANET alanında kullanım alanlarının artmasıyla birlikte VANET ağ güvenliğinin sağlanması daha kritik hale gelmiştir. VANET ağlarına yapılan saldırıları tespit etmekiçin birçok farklı sistem geliştirilmiştir. Makine öğrenimi tabanlı sistemler, bu saldırı tespit sistemlerinin (STSIntrusion Detection Systems IDS) geliştirilmesinde en yaygın kullanılan yöntemlerden biridir. Bu makaledeliteratürde son dönemde yapılan makine öğrenmesi tabanlı VANET IDS üzerine araştırmalar yapılmıştır.Öncelikle VANET mimarisi ve güvenlik gereksinimleri sunulmuş, ardından kapsamlı bir literatür özeti verilmiş ve farklı parametreler üzerinden karşılaştırmalar yapılmıştır. Sonuç olarak, saldırı tespit sistemlerinde birçok farklı makine öğrenmesi modelinin kullanıldığı ve yüksek performanslı tespit gerçekleştirdiği tespit edilmiştir. STS'nin performansında kullanılan makine öğrenmesi algoritmasının yanı sıra birçok farklı parametrenin de performanstakritik rol oynadığı gösterilmiştir. Makale, literatür karşılaştırması (makine öğrenme modeli, simülasyon araçları,  \nReceived: 06/10/2023, Revised: 15/10/2023, Accepted: 07/11/2023  \n1536  \nveri seti, makine öğrenme algoritması ve performans kriterleri gibi kriterler dikkate alınarak) sayesinde saldırı tespit sistemlerinin performansını artıracak kazanımlarla bu alanda yapılacak yeni çalışmalara rehberlik etmeyi amaçlamaktadır.  \nAnahtar Kelimeler: Saldırı tespit sistemleri, Makine öğrenmesi, Güvenlik, Araçsal ağlar  \nI. INTRODUCTION  \nAccording to the statistics in The Global status report about road safety published by the World Health Organization (WHO) in 2018, it is emphasized that the number of people who lost their li","cbCaijqGvTl6Zf7j","https://ap.wps.com/l/cbCaijqGvTl6Zf7j","pdf",1551048,1,21,"English","en",105,"# Abstract\n# Keywords\n# I. Introduction\n## VANET application context and challenges\n## VANET architecture components and communication requirements","[{\"question\":\"What is the focus of the paper on VANET security?\",\"answer\":\"The paper focuses on machine learning-based intrusion detection systems for VANET architectures, aiming to improve detection performance through literature-based insights.\"},{\"question\":\"How does the article evaluate intrusion detection systems?\",\"answer\":\"It reviews recent studies, then compares approaches using parameters such as machine learning model, simulation tools, dataset, and performance criteria.\"},{\"question\":\"What factors influence IDS performance besides the machine learning algorithm?\",\"answer\":\"The paper indicates that many parameters critically affect performance, including factors tied to datasets and evaluation settings, not only the selected ML algorithm.\"}]","A Comprehensive Survey On Machine Learning-Based Intrusion Detection System for Vehicular Area Network Architectures - Comprehensive Survey | PDF",1785683816,53,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-comprehensive-survey-on-machine-learning-based-intrusion-detection-system-for-vehicular-area-network-architectures-comprehensive-survey","",{"@graph":36,"@context":85},[37,54,68],{"@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":53},"https://docshare.wps.com/document/a-comprehensive-survey-on-machine-learning-based-intrusion-detection-system-for-vehicular-area-network-architectures-comprehensive-survey/118481/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the focus of the paper on VANET security?","Question",{"text":75,"@type":76},"The paper focuses on machine learning-based intrusion detection systems for VANET architectures, aiming to improve detection performance through literature-based insights.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the article evaluate intrusion detection systems?",{"text":80,"@type":76},"It reviews recent studies, then compares approaches using parameters such as machine learning model, simulation tools, dataset, and performance criteria.",{"name":82,"@type":73,"acceptedAnswer":83},"What factors influence IDS performance besides the machine learning algorithm?",{"text":84,"@type":76},"The paper indicates that many parameters critically affect performance, including factors tied to datasets and evaluation settings, not only the selected ML algorithm.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]