[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290846-105":59,"doc-detail-290846-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","feature-driven-ddos-detection-in-iot-with-machine-learning-approach","Feature-Driven DDoS Detection in IoT with Machine Learning Approach","","Research strengthening Internet of Things (IoT) security against Distributed Denial-of-Service (DDoS) attacks through a hybrid machine learning pipeline combining Naïve Bayes, K-Nearest Neighbors (KNN), and LightGBM. Using the CICIoT2023 dataset, the study targets vulnerabilities in IoT networks and urgent needs for robust defenses. Correlation-matrix and information-gain feature selection address data-imbalance and complexity, while performance is evaluated via accuracy and false positive rate across Split 1 and Split 2, highlighting KNN and LightGBM as leading options.",{"@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/feature-driven-ddos-detection-in-iot-with-machine-learning-approach/290846/",{"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/feature-driven-ddos-detection-in-iot-with-machine-learning-approach/290846.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Which machine learning algorithms are used in the proposed hybrid approach?","Question",{"text":113,"@type":114},"The study integrates Naïve Bayes, K-Nearest Neighbors (KNN), and LightGBM to detect DDoS attacks in IoT networks.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What dataset is used to evaluate the models?",{"text":118,"@type":114},"The models are evaluated using the CICIoT2023 dataset.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the paper address data imbalance and feature selection challenges?",{"text":122,"@type":114},"It employs feature selection techniques including a correlation matrix and information gain to mitigate issues such as data imbalance and noisy signals.","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},290846,1789642784,{"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":34,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","2024 1st International Conference on Cyber Security and Computing (CyberComp) ©2024 IEEE DOI: 10.1109/CYBERCOMP60759.2024.10913921| 979-8-3503-8772-8/24/$31.00 |   \nFeature-Driven DDoS Detection in IoT with Machine  \nLearning Approach  \nSandra Suriya A/L Jayasuri Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat, Malaysia [sandra.suriya@yahoo.com](sandra.suriya@yahoo.com)  \nSofia Najwa Ramli  \nCenter of Information Security Research Universiti Tun Hussein Onn Malaysia Batu Pahat, Malaysia [sofianajwa@uthm.edu.my](sofianajwa@uthm.edu.my)  \nCik Feresa Mohd Foozy Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat, Malaysia [feresa@uthm.edu.my](feresa@uthm.edu.my)  \nMohd Azahari Mohd Yusof  \nJasin Melaka Campus  \nUniversiti Teknologi Mara  \nMelaka, Malaysia  \n[azahariyusof@uitm.edu.my](azahariyusof@uitm.edu.my)  \nAbstract— This research focuses on strengthening Internet of Things (IoT) security against Distributed Denial-of-Service (DDoS) attacks by implementing a hybrid machine learning approach that integrates Naïve Bayes, K-Nearest Neighbors (KNN), and LightGBM algorithms. Recognizing the critical vulnerabilities ofIoT systems to DDoS attacks and the urgent need for robust security measures, the study evaluates these models using the CICIoT2023 dataset. To address challenges like data imbalance, sophisticated feature selection techniques, including correlation matrix and information gain, are employed. The research rigorously assesses the models' performance in terms of accuracy and false positive rates, aiming to determine the most effective method for mitigating cyber threats and enhancing IoT system resilience. In Split 1, KNN achieves 99.44% accuracy with a false positive rate of 0.05%, LGBM achieves 99.89% accuracy with a false positive rate of 0.04%, while NB lags with 88.67% accuracy and a false positive rate of 0.2%. In Split 2, KNN reaches 99.8% accuracy with a false positive rate of 0.01%, LGBM achieves 99.9% accuracy with no false positives, and NB maintains 89.69% accuracy with a false positive rate of 0.2%. These findings highlight the effectiveness of the machine learning approach in mitigating cyber threats and fortifying IoT system resilience.  \nKeywords— IoT, IoT threats, DDoS, Machine Learning, Information Gain, Correlation Matrix  \nI. INTRODUCTION  \nThe rapid expansion ofthe IoT has ushered in a new era of interconnected devices, spanning various sectors like agriculture, healthcare, automotive, and smart homes. However, this proliferation of IoT devices has also brought many security concerns. With the number of IoT devices projected to soar to 25.44 billion by 2030 [1], the vulnerabilities in IoT networks have become a prime target for malicious actors. Among the myriads of security threats plaguing IoT networks, common attacks include botnets, distributed denial-of-service (DDoS), infiltration, identity and data theft, and ransomware [2] . One infamous example is the Mirai botnet, which explicitly targets IoT devices such as cameras, routers, and smart home appliances [3] . These attacks not only compromise user data but also pose risks of disrupting critical infrastructure and spreading across interconnected systems. To counteract these threats, researchers  \nAminu Abdulkadir Mahmoud  \nDepartment of Cyber Security  \nYusuf Maitama Sule University  \nKano, Nigeria [aamahmoudyola@yumsuk.edu.ng](aamahmoudyola@yumsuk.edu.ng)  \nare turning to artificial intelligence (AI), particularly machine learning, as a potent tool for bolstering IoT security [4] .  \nMachine learning algorithms, encompassing supervised, unsupervised, and reinforcement learning techniques, are adept at analyzing large datasets to discern patterns and anomalies in IoT network traffic [5] . For instance, supervised learning algorithms classify data based on labeled examples, while unsupervised. Learning techniques group similar data points wit","cbCaisbtjDibBmzR","https://ap.wps.com/l/cbCaisbtjDibBmzR","pdf",407852,"English","# Abstract\n# Keywords\n# I. INTRODUCTION","[{\"question\":\"Which machine learning algorithms are used in the proposed hybrid approach?\",\"answer\":\"The study integrates Naïve Bayes, K-Nearest Neighbors (KNN), and LightGBM to detect DDoS attacks in IoT networks.\"},{\"question\":\"What dataset is used to evaluate the models?\",\"answer\":\"The models are evaluated using the CICIoT2023 dataset.\"},{\"question\":\"How does the paper address data imbalance and feature selection challenges?\",\"answer\":\"It employs feature selection techniques including a correlation matrix and information gain to mitigate issues such as data imbalance and noisy signals.\"}]","Feature-Driven DDoS Detection in IoT with Machine Learning Approach | PDF",18]