[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121124-en":3,"doc-seo-121124-105":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},121124,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A Machine Learning-Based Approach for the Detection of DDoS Attacks on Internet of Things Using CICDDoS2019 - Paper","Rapid IoT expansion increases reliance on cloud connectivity while simultaneously amplifying exposure to cyber threats, where DDoS and DoS attacks are among the most disruptive. Conventional intrusion detection approaches often struggle to reliably identify DDoS traffic. This study proposes a machine learning model for DDoS detection on IoT networks using the freely available CICDDoS2019 dataset, exploring multiple classifiers to capture distinguishing traffic characteristics. AdaBoost and XGBoost achieve exceptional results, and a future hybrid approach will further enhance performance through continuous model updates.","| \u003Cbr>\u003Cbr>Sharif et al. LGURJCSIT 2024\u003Cbr>LGU Research Journal of Computer Science & IT\u003Cbr>ISSN: 2521-0122 (Online)\u003Cbr>ISSN: 2519-7991 (Print)\u003Cbr>doi: 10.54692/lgurjcsit.2024.082569\u003Cbr>Vol (8): Issue (2), April  June 2024 |  |\n| --- | --- |\n|  |  |\n\nA Machine Learning-Based Approach for the Detection of DDoS Attackson the Internet of Things Using CICDDoS2019 Dataset – PortMap  \nHanan Sharif1, Sardar Usman2, Muhammad Hasnain3, Shagufta Anwar4, Mohammed Nawaf Altouri5, Fahad Mohammed Sharahili6, M. Usman Ashraf 7*  \n1,3,4Department of Computer Science, Leads University Lahore, Punjab, Pakistan. 2Department of Computer Science Software Engineering & IT, Grand Asian University, Sialkot, Punjab,  \nPakistan.  \n5University of prince muqrin, Madinah, Saudi Arabia.  \n6Imam mohammad Bn Saud Islamic University, Riyadh, Saudi Arabia.  \n7Department of Computer Science, GC Women University Sialkot, Punjab, Pakistan,  \n[Email: usman.ashraf@gcwus.edu.pk](Email: usman.ashraf@gcwus.edu.pk)  \nABSTRACT:  \nIn today's technological era, the Internet has become ubiquitous, playing a vital role in our daily lives. With the exponential growth ofIoT innovation, millions of interconnected IoT-enabled devices rely on cloud services to communicate over the Internet. However, this rapid development also exposes these devices to various threats, with DDoS (Distributed Denial of Service) and DoS (Denial of Service) attacks being particularly potent and destructive. DDoS attacks present a unique challenge as they are tough to detect using conventional intrusion detection frameworks and traditional methodologies. Fortunately, advancements in machine learning have provided a promising solution by enabling accurate differentiation between DDoS attacks and other forms of data. This studyproposes a DDoS detection model based on machine learning algorithms. We used the most recent and freely available online dataset called CICDDoS2019 to conduct this study. Various machine learning-based techniques were explored to identify the characteristics associated with accurate classification. Among the algorithms tested, AdaBoost and XGBoost demonstrated exceptional performance. A hybrid approach will be incorporated into this model as part of future work, further improving its capabilities. It is worth noting that this model will be continuously updated with new data on DDoS attacks, ensuring its relevance and effectiveness in combating emerging threats. By leveraging machine learning techniques, this approach enhances the detection of DDoS attacks on Internet of Things networks, safeguarding the integrity and security of connected devices and the overall IoTecosystem.  \nKEYWORDS: DDoS, Internet of Things, Machine Learning, Classification, DDoS Detection, CICDDoS2019 .  \n1. INTRODUCTION  \nThe Internet of Things (IoT) continues revolutionizing our world, bringing numerous benefits and advancements. Today, IoT devices play a pivotal role in our daily lives, permeating various aspects such as smart cities, electricity grids, homes, vehicles, construction machinery, and hospitals. This exponential growth in digital technology  \naims to enhance our lives by seamlessly integrating physical devices with digital intelligence, creating a more comfortable, intelligent, and manageable environment. IoT devices collect vast amounts of data, which can be shared through the Internet, enabling access from anywhere at any time. These data streams are typically stored and accessed through integrated  \nLGU Research Journal of Computer Science & Information Technology, Vol (8): Issue (2), LGURJCSIT 19  \ncloud platforms, facilitating communication among IoT devices. Research indicates that by 2030, the number of IoT devices is projected to reach 20 billion, with the current count already at 10.07 billion, all interconnected through the web [1] . However, with this extensive proliferation of interconnected devices comes the need to protect the data they generate. Cyber security is crucial to prevent unauthorized acce","cbCaistW7289YN3j","https://ap.wps.com/l/cbCaistW7289YN3j","pdf",19516151,1,12,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"What problem does the study address for IoT security?\",\"answer\":\"The study addresses the difficulty of detecting DDoS attacks in IoT environments, which are more complex and destructive than other cyber threats.\"},{\"question\":\"Which dataset is used to train and evaluate the model?\",\"answer\":\"The study uses the CICDDoS2019 dataset, divided into harmful and harmless classes.\"},{\"question\":\"Which machine learning algorithms performed best in the experiments?\",\"answer\":\"AdaBoost and XGBoost demonstrated exceptional performance among the tested algorithms.\"}]","A Machine Learning-Based Approach for the Detection of DDoS Attacks on Internet of Things Using CICDDoS2019 - Paper | PDF",1785733875,30,{"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-machine-learning-based-approach-for-the-detection-of-ddos-attacks-on-internet-of-things-using-cicddos2019-paper","",{"@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-machine-learning-based-approach-for-the-detection-of-ddos-attacks-on-internet-of-things-using-cicddos2019-paper/121124/",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 study address for IoT security?","Question",{"text":75,"@type":76},"The study addresses the difficulty of detecting DDoS attacks in IoT environments, which are more complex and destructive than other cyber threats.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset is used to train and evaluate the model?",{"text":80,"@type":76},"The study uses the CICDDoS2019 dataset, divided into harmful and harmless classes.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms performed best in the experiments?",{"text":84,"@type":76},"AdaBoost and XGBoost demonstrated exceptional performance among the tested algorithms.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":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":29,"slug":121},"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":106,"slug":137},19,"General","general"]