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This research improves IoT device security by assessing the likelihood of network attacks using ordinary device network data and attack network data derived from comparable statistics. IoT devices for smart healthcare were used to generate traffic via botnet attacks, and collected data were analyzed with Pearson correlation and entropy to extract features. Machine learning models were applied to classify normal and attack traffic, with the BoT-IoT dataset cross-evaluated for authentication, supporting more effective detection and mitigation strategies.",{"@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/enhancing-iot-device-security-through-network-attack-data-analysis-using-machine-learning-algorithms/291184/",{"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/enhancing-iot-device-security-through-network-attack-data-analysis-using-machine-learning-algorithms/291184.png","ImageObject",300,407,{"name":92,"@type":93},"Aurelia","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",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of the research on IoT device security?","Question",{"text":113,"@type":114},"The study aims to enhance the security of IoT devices by investigating the likelihood of network attacks using device network data and corresponding attack data.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was data collected for training and analysis?",{"text":118,"@type":114},"IoT devices dedicated to smart healthcare systems were used, and botnet attacks were conducted to generate network data. The collected traffic was then analyzed to extract relevant features.",{"name":120,"@type":111,"acceptedAnswer":121},"Which techniques were used to distinguish normal traffic from attack traffic?",{"text":122,"@type":114},"The approach uses statistical measures such as the Pearson coefficient and entropy to extract features, then applies machine learning algorithms with data preprocessing to categorize normal versus attack traffic.","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},291184,1789643615,{"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":41,"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},1099514068365,"https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1794309523707217728","future internet  \nArticle  \nEnhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms  \nAshish Koirala 1, Rabindra Bista 1,* and Joao C. Ferreira 2,3  \nCitation: Koirala, A.; Bista, R.; Ferreira, J.C. Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms. Future Internet 2023, 15, 210. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/](10.3390/)fi[15060210](15060210)  \nAcademic Editor: Gyu Myoung Lee  \nReceived: 8 May 2023  \nRevised: 30 May 2023  \nAccepted: 7 June 2023  \nPublished: 9 June 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science and Engineering, Kathmandu University, Dhulikhel 45200, Nepal; [ashish.krk4@gmail.com](ashish.krk4@gmail.com)  \n2 Inov Inesc Inovaç¢o—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal; [jcafa@iscte.pt](jcafa@iscte.pt)  \n[3](3 Instituto Universit)[ Instituto Universit](3 Instituto Universit)¡rio de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, Portugal  \n* Correspondence: [rbista@ku.edu.np](rbista@ku.edu.np)  \nAbstract: The Internet of Things (IoT) shares the idea of an autonomous system responsible for transforming physical computational devices into smart ones. Contrarily, storing and operating information and maintaining its confidentiality and security is a concerning issue in the IoT. Throughout the whole operational process, considering transparency in its privacy, data protection, and disaster recovery, it needs state-of-the-art systems and methods to tackle the evolving environment. This research aims to improve the security of IoT devices by investigating the likelihood of network attacks utilizing ordinary device network data and attack network data acquired from similar statistics. To achieve this, IoT devices dedicated to smart healthcare systems were utilized, and botnet attacks were conducted on them for data generation. The collected data were then analyzed using statistical measures, such as the Pearson coefficient and entropy, to extract relevant features. Machine learning algorithms were implemented to categorize normal and attack traffic with data preprocessing techniques to increase accuracy. One of the most popular datasets, known as BoT-IoT, was cross-evaluated with the generated dataset for authentication of the generated dataset. The research provides insight into the architecture of IoT devices, the behavior of normal and attack networks on these devices, and the prospects of machine learning approaches to improve IoT device security. Overall, the study adds to the growing body of knowledge on IoT device security and emphasizes the significance of adopting sophisticated strategies for detecting and mitigating network attacks.  \nKeywords: Internet of Things (IoT); botnet; pearson coefficient; random forest; ensemble learning  \n1. Introduction  \nIoT devices represent a paradigm shift in the way we interact digitally with the physical world, as it enables the connectivity and communication between a vast array of devices and systems through a communication network. These interconnected devices, which can range from simple sensors to complex machinery, are equipped with the ability to gather, process, and transmit data, allowing them to sense and respond to their surroundings inan autonomous manner. The IoT shares the idea of an autonomous system responsible for transforming physical computational devices into smart ones in an activity-generated environment under existing networks through embedded systems. Embedded systems provide the essential combination of hardware and firmware, along with Internet access, to execute respec","cbCaijQ8EUEho8pO","https://ap.wps.com/l/cbCaijQ8EUEho8pO","pdf",4320816,"English","# Introduction\n## IoT devices and security concerns\n# Methodology and data generation\n## Botnet attack traffic on IoT healthcare systems\n## Feature extraction with statistical measures\n# Machine learning for traffic classification\n## Preprocessing and model-based detection\n# Dataset evaluation\n## BoT-IoT cross-evaluation\n# Findings and significance","[{\"question\":\"What is the main goal of the research on IoT device security?\",\"answer\":\"The study aims to enhance the security of IoT devices by investigating the likelihood of network attacks using device network data and corresponding attack data.\"},{\"question\":\"How was data collected for training and analysis?\",\"answer\":\"IoT devices dedicated to smart healthcare systems were used, and botnet attacks were conducted to generate network data. The collected traffic was then analyzed to extract relevant features.\"},{\"question\":\"Which techniques were used to distinguish normal traffic from attack traffic?\",\"answer\":\"The approach uses statistical measures such as the Pearson coefficient and entropy to extract features, then applies machine learning algorithms with data preprocessing to categorize normal versus attack traffic.\"}]","Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms | PDF",76]