[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127248-en":3,"doc-seo-127248-105":30,"detail-sidebar-cat-0-en-105":92},{"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":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},127248,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","IoT intrusion detection system based on machine learning and deep learning - research article","IoT device proliferation intensifies cybersecurity risks, making robust intrusion detection essential to defend against botnets and Distributed Denial-of-Service attacks. The study benchmarks machine learning and deep learning models using BoT-IoT and CIC-IDS2017, assessing accuracy and other evaluation metrics. Results show XGBoost delivers top performance among ML models, while CNN-based deep learning achieves high detection accuracy, emphasizing that preprocessing substantially improves model effectiveness. The findings support real-time, scalable intrusion detection for IoT environments.","Research Article  \nIoT intrusion detection system based on machine learning  \nand deep learning  \n1Karrar Majid Jasim  \nInformatic Institute for Postgraduate Studies  \nUniversity of Information Technology and Communications Baghdad, Iraq [karrarmajidjasim@gmail.com](karrarmajidjasim@gmail.com)[ ](karrarmajidjasim@gmail.com)[Ms202310757@iips.edu.iq](Ms202310757@iips.edu.iq)  \n2A.P.Dr. Joolan Rokan Nayef Informatic Institute for Postgraduate Studies  \nUniversity of Information Technology and Communications Baghdad, Iraq [newjolan@gmail.com](newjolan@gmail.com)[ ](newjolan@gmail.com)[dr.jolan_alkhazraji@iips.edu.iq](dr.jolan_alkhazraji@iips.edu.iq)  \nA R T I C L E I N F O  \nArticle History  \nReceived: 09/02/2025  \nAccepted: 02/04/2025  \nPublished: 05/06/2025 This is an open-access article under the CC BY 4.0 license:  \n[http://creativecommons](http://creativecommons). org/licenses/by/4.0/  \nABSTRACT  \nThe proliferation of Internet of Things IoT devices has amplified cybersecurity challenges, necessitating robust Intrusion Detection Systems IDS to safeguard against threats such as botnets and Distributed Denial-of-Service DDoS attacks. This paper evaluates the performance of Machine Learning ML and Deep Learning DL models on two benchmark datasets, BoT-IoT and CIC-IDS2017, to develop efficient IDS. Among ML models, XGBoost demonstrated the best performance, achieving 99.99% accuracy on BoT-IoT and 99.91% on CIC-IDS2017 with superior computational efficiency. For DL, Convolutional Neural Networks CNNs achieved 99.99% accuracy on BoT-IoT and 99.61% on CIC-IDS2017 with preprocessing, highlighting the critical role of data preparation. These findings underline the effectiveness of advanced ML/DL models and preprocessing techniques in enhancing IoT security, providing a pathway for real-time, scalable intrusion detection in IoT environments.  \nKeywords: IoT Security; Intrusion Detection; Machine Learning; Deep Learning.  \n1. INTRODUCTION  \nInformation systems require robust protection from unauthorized access, misuse, and errors a concept central to cybersecurity, which safeguards data, services, and infrastructure [1]. Among the many innovations shaping our world, the IoT stands out as a transformative network of interconnected devices. By enabling smart environments like cities and healthcare, IoT improves connectivity, but also introduces significant vulnerabilities due to limited device resources and inadequate standardization [2] . The rapid expansion of IoT has heightened cybersecurity concerns, drawing increased attention from academia and industry to detect and prevent potential attacks [3] .  \nA prominent threat within this landscape is botnets—networks of compromised devices controlled by attackers to execute harmful activities such as DDoS attacks, spam distribution, and data theft. These botnets, managed through command-and-control servers, disrupt online services and infrastructure at an alarming scale [4] . To counteract such threats, Intrusion Detection Systems IDS are deployed to monitor for security breaches. IDS employ various methods, such as signature-based detection for known threats or specification-based approaches to analyze traffic behavior, though improving accuracy and reducing false alarms remain ongoing challenges [5] .  \nAdvancements in technology have further empowered defense mechanisms. Machine learning, through data-driven models, detects anomalies by identifying unusual patterns indicative of intrusions. Meanwhile, deep learning, a subset of machine learning, enhances detection accuracy with techniques like CNNs and autoencoders, automating analyses and uncovering IoT software vulnerabilities. Together, these innovations are pivotal in addressing the growing landscape of cyber threats [5] .  \n2. EVALUATION METRICS  \nPerformance for machine learning models KNN, SVM, RF, XGBoost and deep learning model CNN for intrusion pattern classification in the BoT-IoT and CIC-IDS2017 datasets is measured in terms ","cbCaieUMltdKXUHX","https://ap.wps.com/l/cbCaieUMltdKXUHX","pdf",741543,1,11,"English","en",105,"# Article History\n# Abstract\n# Keywords\n# Introduction\n# Evaluation Metrics\n# Related Works","[{\"question\":\"What security problem does the IoT intrusion detection system target?\",\"answer\":\"It targets cybersecurity threats in IoT environments, especially botnets and DDoS-style attacks, by detecting malicious intrusions and monitoring traffic for breaches.\"},{\"question\":\"Which datasets are used to evaluate the machine learning and deep learning models?\",\"answer\":\"The models are evaluated on two benchmark datasets: BoT-IoT and CIC-IDS2017.\"},{\"question\":\"How do the reported results compare between ML and DL models?\",\"answer\":\"Among machine learning models, XGBoost achieves the best reported accuracy on both datasets. For deep learning, CNNs achieve very high accuracy, and preprocessing is highlighted as a critical factor for performance.\"}]","IoT intrusion detection system based on machine learning and deep learning - research article | PDF",1785937728,28,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"iot-intrusion-detection-system-based-on-machine-learning-and-deep-learning-research-article","",{"@graph":36,"@context":86},[37,54,69],{"@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/iot-intrusion-detection-system-based-on-machine-learning-and-deep-learning-research-article/127248/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What security problem does the IoT intrusion detection system target?","Question",{"text":76,"@type":77},"It targets cybersecurity threats in IoT environments, especially botnets and DDoS-style attacks, by detecting malicious intrusions and monitoring traffic for breaches.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which datasets are used to evaluate the machine learning and deep learning models?",{"text":81,"@type":77},"The models are evaluated on two benchmark datasets: BoT-IoT and CIC-IDS2017.",{"name":83,"@type":74,"acceptedAnswer":84},"How do the reported results compare between ML and DL models?",{"text":85,"@type":77},"Among machine learning models, XGBoost achieves the best reported accuracy on both datasets. 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