[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121552-en":3,"doc-seo-121552-105":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},121552,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Detection of DDoS Attacks in IoT Networks Using Machine Learning Algorithms - Feature Selection and Model Evaluation","The paper addresses the security risks introduced by rapidly expanding Internet of Things (IoT) deployments, focusing on Distributed Denial of Service (DDoS) attacks that can exhaust network resources and disrupt critical services. A robust detection framework is proposed using machine learning models combined with feature selection and dimensionality reduction. Experiments on the CICIoT2023 dataset apply Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA) with Random Forest, SVM, Naïve Bayes, XGBoost, and KNN, trained and validated via k-fold cross-validation.","2024 IEEE 9th International Conference on Engineering Technologies and Applied Sciences (ICETAS) ©2024 IEEE DOI: 10.1109/ICETAS62372.2024.11120083| 979-8-3503-6314-2/24/$31.00 |   \n2024 IEEE 9th International Conference on Engineering Technologies and Applied Sciences (ICETAS)  \nDetection of DDoS Attacks in IoT Networks Using Machine Learning Algorithms  \nAlwan Ahmed Abdulrahman Alwan Kulliyyah Information & Communications Technology International Islamic University Malaysia, Kuala Lumpur, Malaysia [ahmed.alwan@live.iium.edu](ahmed.alwan@live.iium.edu). my  \n2nd Asadullah Shah  \nKulliyyah Information & Communications Technology International Islamic  \nUniversity [asadullah@iium.edu.my](asadullah@iium.edu.my)  \nAbstract—The rapid proliferation of Internet of Things (IoT) devices has revolutionized various industries by enabling seamless connectivity and data exchange. However, this connectivity also introduces significant security challenges, particularly in the form of Distributed Denial of Service (DDoS) attacks. These attacks can overwhelm IoT networks, leading to service disruptions and substantial financial losses. This paper presents a robust and efficient framework for detecting DDoS attacks in IoT networks using advanced machine learning techniques and effective featureselection methods. The study utilizes the CICIoT2023 dataset and employs Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA) to enhance the performance of machine learning models, including Random Forest, Support Vector Machine (SVM), Na¨ıve Bayes, XGBoost, and K-Nearest Neighbors (KNN). The models are trained and validated using k-fold cross-validation to ensure robustness and generalizability. Expected results indicate significant improvements in detection accuracy, precision, recall, and computational efficiency. The findings underscore the importance of featureselection in improving model performance and provide valuable insights into the strengths and weaknesses of different machine learning models. This research contributes to the development of scalable and effective DDoS detection solutions for IoT networks, ensuring their reliability and resilience against evolving cyber threats. Future work will focus on exploring additional feature selection methods, integrating deep learning techniques, and validating the models in real-world IoT environments.  \nIndex Terms—Internet of Things (IoT), Distributed Denial of Service (DDoS) attacks, Machine learning, Feature selection  \nI. INTRODUCTION  \nThe Internet of Things (IoT) is revolutionizing various industries by connecting billions of devices globally, enabling seamless communication and data exchange. By 2025, it is estimated that over 75 billion IoT devices will be in use, generating immense volumes of data and fostering innovative applications across sectors such as healthcare, smart cities, and industrial automation [1] . Despite its transformative potential, the expansive connectivity of IoT networks introduces significant security challenges, with Distributed Denial of Service (DDoS) attacks being one of the most severe threats [2] . DDoS attacks disrupt the normal functioning of IoT networks by overwhelming devices with excessive traffic, rendering critical services unavailable. A study by Symantec [3] revealed that IoT devices experience an attack every two minutes on average, highlighting the urgency of addressing this issue. The  \n3rdAlwan Abdullah  \nAbdulrahman Alwan National Advanced IPv6 Centre, Universiti Sains  \nMalayisa Penang,  \nMalaysia [Alwan.aa@student.usm.my](Alwan.aa@student.usm.my)  \n4th Shams Ul Arfeen Laghari  \nNational Advanced IPv6 Centre, Universiti Sains Malayisa Penang, Malaysia [shamsularfeen@usm.my](shamsularfeen@usm.my)  \nfinancial implications are equally staggering, with IoTrelated cyberattacks projected to cost businesses over $300 billion annually, according to recent reports [4], [5] . As IoT devices often operate with limited computational res","cbCaiejbhPeRNZMG","https://ap.wps.com/l/cbCaiejbhPeRNZMG","pdf",265727,1,5,"English","en",105,"# Introduction\n## Background and motivation\n## Research objectives\n# Abstract and index terms","[{\"question\":\"What problem does the paper focus on in IoT networks?\",\"answer\":\"It focuses on detecting Distributed Denial of Service (DDoS) attacks that can overwhelm IoT networks with excessive traffic and make services unavailable.\"},{\"question\":\"Which dataset and feature selection techniques are used?\",\"answer\":\"Experiments use the CICIoT2023 dataset and apply Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA) to improve model performance and efficiency.\"},{\"question\":\"Which machine learning models are evaluated for DDoS detection?\",\"answer\":\"The study evaluates Random Forest, Support Vector Machine (SVM), Naïve Bayes, XGBoost, and K-Nearest Neighbors (KNN), using k-fold cross-validation for training and validation.\"}]","Detection of DDoS Attacks in IoT Networks Using Machine Learning Algorithms - Feature Selection and Model Evaluation | PDF",1785736214,13,{"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},"detection-of-ddos-attacks-in-iot-networks-using-machine-learning-algorithms-feature-selection-and-model-evaluation","",{"@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/detection-of-ddos-attacks-in-iot-networks-using-machine-learning-algorithms-feature-selection-and-model-evaluation/121552/",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","2026-08-03",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 problem does the paper focus on in IoT networks?","Question",{"text":75,"@type":76},"It focuses on detecting Distributed Denial of Service (DDoS) attacks that can overwhelm IoT networks with excessive traffic and make services unavailable.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and feature selection techniques are used?",{"text":80,"@type":76},"Experiments use the CICIoT2023 dataset and apply Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA) to improve model performance and efficiency.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are evaluated for DDoS detection?",{"text":84,"@type":76},"The study evaluates Random Forest, Support Vector Machine (SVM), Naïve Bayes, XGBoost, and K-Nearest Neighbors (KNN), using k-fold cross-validation for training and validation.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"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":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]