[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125974-en":3,"doc-seo-125974-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125974,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Innovative Machine Learning Strategies for DDoS Detection - A Review","This broad survey investigates machine learning (ML) methods for detecting distributed denial of service (DDoS) attacks and addresses limitations of traditional intrusion detection systems, especially for application-layer DDoS targeting legitimate web traffic patterns. The review summarizes recent studies using datasets such as UNSW-NB-15, CICDDoS2019, and LATAM-DDoS-Internet of Things, reporting strong accuracy and performance metrics. It further covers advanced designs including protocol-based deep intrusion detection, autoencoder-multi-layer perceptron, early noise-robust models, and distributed frameworks, highlighting techniques from ensemble optimization and anomaly detection to hyperband-tuned deep networks and evolutionary SVMs in cloud and SDN environments.","| REVIEW ARTICLE |  | UHD JOURNAL OF SCIENCE AND TECHNOLOGY |\n| --- | --- | --- |\n\nInnovative Machine Learning Strategies for DDoS Detection: A Review  \nOmar Mohammed Amin Ali1, Rebin Abdulkareem Hamaamin2, Barzan Jalal Youns3, Shahab Wahhab Kareem3  \n1Department of IT, Chamchamal Technical Institute, Sulaimani Polytechnic University, KRG, Iraq.  \n2Computer Science, College of Sciences, Charmo University, Chamchamal, Sulaimani, KRG, Iraq.  \n3Department of Technical Information Systems Engineering, Technical Engineering College, Erbil Polytechnic University, KRG, Iraq.  \nA B S T R A C T  \nThis is a broad survey that investigates the use of machine learning (ML) methods for detecting distributed denial of service (DDoS) attacks. Traditional intrusion detection systems face difficulties in application-layer DDoS attacks because they target legal web traffic forms using standard transmission control protocol connections. This paper reviews different ML methods used in recent studies to tackle these issues. These studies use various data sets, such as UNSW-np-15, CICDDoS2019, and the novel dataset LATAM-DDoS-Internet of Things. , which prove the efficacy of the proposed models in terms of accuracy and performance metrics. The second group of studies shows more advanced designs, such as protocol-based deep intrusion detection and autoencoder-multi-layer perceptron. These use deep learning to find features and group attacks. All of these approaches present favorable outcomes when it comes to distinguishing normal, DoS, and DDoS traffic with a high level of accuracy. Furthermore, the review discusses works that emphasize the early detection of noise-robust models and distributed frameworks. Different techniques, such as snake optimizer with ensemble learning, metastability theory, and spark-based anomaly detection, highlight the trend of predicting DDoS attacks, whereas hyperband-tuned deep neural networks and evolutionary support vector machine models show higher accuracy in cloud systems as well as software-defined networking environments. Hence, this review gives a general observation of how DDoS attacks develop on their way and proves that ML techniques help to strengthen network security.  \nIndex Terms: Distributed Denial of Service Attacks, Machine Learning, Internet of Things, Deep Learning, Anomaly Detection  \n1. INTRODUCTION  \nDistributed denial of service (DDoS) attacks pose a significant threat in the current era of interdependent systems and digital dependencies, where cyber challenges are already numerous. It is understandable that nefarious actors remain  \n\n| Access this article online |  |\n| --- | --- |\n| DOI: 10.21928/uhdjst.v8n2y2024 . pp38-49 | E-ISSN: 2521-4217\u003Cbr>P-ISSN: 2521-4209 |\n| Copyright © 2024 Ali et al. This is an open access article distributed under the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (CC BY-NC-ND 4 .0) |  |\n\nresolute in exploiting network infrastructure vulnerabilities, necessitating the need for smarter and more adaptive defense mechanisms. This urgent need has fueled intensive research into DDoS attack detection using Machine learning (ML) methodologies, resulting in an active and rapidly evolving field of study teeming with innovative solutions. We aim to investigate the intricate web of studies dealing with MLand DDoS attack detection through this in-depth analysis. This review aims to give This review seeks to provide a comprehensive overview of cutting-edge methodologies, challenges, and advancements in this crucial field, utilizing 19 diverse references that each contribute to the overall discussion the literature review uses a wide range of DDoS  \nCorresponding author’s e-mail: [omar.mohammed@spu.edu.iq](omar.mohammed@spu.edu.iq)  \nReceived: 28-07-2024 Accepted: 12-09-2024 Published: 02-10-2024  \n38 UHD Journal of Science and Technology | Jul 2024 | Vol 8 | Issue 2  \nOmar, et al.: DDoS Detection  \ndetection and ML algorithms, from classical classifiers, suc","cbCairksDji5xhz1","https://ap.wps.com/l/cbCairksDji5xhz1","pdf",771301,7,1,12,"English","en",105,"# Abstract\n# Introduction\n# Methods and ML Approaches\n## Datasets and Evaluation\n## Deep Learning and Feature Extraction\n## Noise-Robust and Distributed Frameworks\n## Cloud and SDN-Oriented Techniques","[{\"question\":\"Why is DDoS detection challenging for traditional intrusion detection systems?\",\"answer\":\"Traditional systems struggle with application-layer DDoS because attackers mimic legitimate web traffic forms and standard TCP connections.\"},{\"question\":\"Which datasets are referenced for evaluating DDoS detection models?\",\"answer\":\"The review cites datasets including UNSW-NB-15, CICDDoS2019, and LATAM-DDoS-Internet of Things.\"},{\"question\":\"What advanced ML and deep learning designs does the review describe?\",\"answer\":\"It discusses protocol-based deep intrusion detection, autoencoder-multi-layer perceptron, and related approaches that learn features and group attacks.\"}]","Innovative Machine Learning Strategies for DDoS Detection - A Review | PDF",1785902331,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"innovative-machine-learning-strategies-for-ddos-detection-a-review","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/innovative-machine-learning-strategies-for-ddos-detection-a-review/125974/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is DDoS detection challenging for traditional intrusion detection systems?","Question",{"text":77,"@type":78},"Traditional systems struggle with application-layer DDoS because attackers mimic legitimate web traffic forms and standard TCP connections.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which datasets are referenced for evaluating DDoS detection models?",{"text":82,"@type":78},"The review cites datasets including UNSW-NB-15, CICDDoS2019, and LATAM-DDoS-Internet of Things.",{"name":84,"@type":75,"acceptedAnswer":85},"What advanced ML and deep learning designs does the review describe?",{"text":86,"@type":78},"It discusses protocol-based deep intrusion detection, autoencoder-multi-layer perceptron, and related approaches that learn features and group attacks.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,121,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":30,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":108,"slug":138},19,"General","general"]