[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126155-en":3,"doc-seo-126155-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126155,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Securing constrained IoT systems - A lightweight machine learning approach for anomaly detection and prevention","With the growth of IoT, edge, and fog computing, cyber attacks evolve in sophistication while IoT deployments face severe resource constraints such as limited energy and memory. This work leverages Tiny Machine Learning to build an ML-based mechanism that categorizes and detects resource-constrained attacks across device, edge, and cloud settings. Comparative evaluation across multiple ML strategies measures training efficiency, energy use, and memory consumption, demonstrating that a Decision Tree model on smart devices achieves strong performance, with accuracy above 96.9% for detecting resource-limit attacks.","This is an electronic reprint of the original article.  \nThis reprint may differ from the original in pagination and typographic detail.  \nAlwaisi, Zainab; Kumar, Tanesh; Harjula, Erkki; Soderi, Simone  \nSecuring constrained IoT systems: A lightweight machine learning approach for anomaly detection and prevention  \nPublished in:  \nInternet of Things (The Netherlands)  \nDOI:  \n10.1016/j.iot.2024.101398  \nPublished: 01/12/2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublished under the following license:  \nCC BY  \nPlease cite the original version:  \nAlwaisi, Z. , Kumar, T. , Harjula, E. , & Soderi, S. (2024) . Securing constrained IoT systems: A lightweight machine learning approach for anomaly detection and prevention. Internet of Things (The Netherlands) , 28 , Article  \n101398. [https://doi.org/10.1016/j.iot.2024.101398](https://doi.org/10.1016/j.iot.2024.101398)  \nThis material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you foryour research use or educational purposes in electronic or print form. You must obtain permission for anyother use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user.  \nInternet of Things 28 (2024) 101398  \n| Research article\u003Cbr>Securing constrained IoT systems: A lightweight machine learning approach for anomaly detection and prevention\u003Cbr>Zainab Alwaisia,∗, Tanesh Kumarc, Erkki Harjulab, Simone Soderiaa IMT School For Advanced Studies, Italy\u003Cbr>b Centre for Wireless Communications, University of Oulu, Finland c School of Electrical Engineering, Aalto University, Finland |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Internet of Things (IoT)\u003Cbr>Smart devices Energy Memory\u003Cbr>Resource constraints\u003Cbr>Detection TinyML ML Edge AI |  | With the advent of advanced technological developments such as IoT, edge, and fog computing, cyber attacks have become increasingly sophisticated. IoT networks facilitate collaborative and intelligent tasks across various domains, including Industry 4.0, digital healthcare, and home automation. However, the proliferation of IoT devices has raised concerns about severe attacks, particularly those targeting resource constraints such as energy and memory. In response to these challenges, Tiny Machine Learning (TinyML) has emerged as a new research area, focusing on machine learning techniques tailored for embedded and IoT systems. This study proposes an ML detection mechanism designed to categorize and detect resource-constrained attacks in IoT devices. We consider IoT devices to be integral components within the continuum of edge and cloud computing, leveraging EdgeML and CloudML for detection purposes. Our paper conductsa comparative analysis of ML models, with a specific focus on energy consumption and memory usage in IoT applications. We compare various ML methodologies, including cloud-based, edgebased, and device-based strategies for both training and detection. The evaluation encompasses the application of these ML techniques to petite IoT devices, utilizing TinyML, as well as cloud and edge devices. Our findings reveal that the Decision Tree algorithm deployed on smart devices surpasses other approaches in terms of training efficiency, resource utilization, and the ability to detect resource-constrained attacks on IoT devices. We demonstrate a high level of accuracy, exceeding 96.9%, across all presented ML models in detecting resource constraint attacks within IoT systems. In summary, this research serves as a guide for implementing effective security measures in the dynamic landscape of IoT and associated technologies. |\n\n1. Introduction  \nThe Internet of Things (IoT) encompasses a vast array of interconnected devices ranging from everyday items like toastersand toothbrush","cbCaii6KnTsyPtBe","https://ap.wps.com/l/cbCaii6KnTsyPtBe","pdf",2758888,5,1,21,"English","en",105,"# Introduction\n## Resource constraints in IoT and need for efficient security\n## Edge/fog computing and ML-enabled detection\n# Keywords and research focus\n## Tiny Machine Learning for constrained devices\n# Comparative evaluation\n## ML training efficiency, energy, and memory usage\n## Detection performance across device, edge, and cloud","[{\"question\":\"What problem does the study address in constrained IoT environments?\",\"answer\":\"It targets cyber attacks that are especially challenging in IoT systems with limited energy and memory. The goal is to detect and prevent resource-constrained attacks without exceeding device limitations.\"},{\"question\":\"How does the approach use Tiny Machine Learning?\",\"answer\":\"It proposes an ML detection mechanism based on TinyML techniques tailored for embedded/IoT systems. The detection is applied using device-, edge-, and cloud-oriented ML workflows.\"},{\"question\":\"Which machine learning model performs best and what accuracy is reported?\",\"answer\":\"The Decision Tree algorithm running on smart devices outperforms other approaches in training efficiency, resource utilization, and detection capability. Reported accuracy exceeds 96.9% across the presented models for resource constraint attack detection.\"}]","Securing constrained IoT systems - A lightweight machine learning approach for anomaly detection and prevention | PDF",1785903444,53,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"securing-constrained-iot-systems-a-lightweight-machine-learning-approach-for-anomaly-detection-and-prevention","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/securing-constrained-iot-systems-a-lightweight-machine-learning-approach-for-anomaly-detection-and-prevention/126155/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What problem does the study address in constrained IoT environments?","Question",{"text":77,"@type":78},"It targets cyber attacks that are especially challenging in IoT systems with limited energy and memory. The goal is to detect and prevent resource-constrained attacks without exceeding device limitations.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the approach use Tiny Machine Learning?",{"text":82,"@type":78},"It proposes an ML detection mechanism based on TinyML techniques tailored for embedded/IoT systems. The detection is applied using device-, edge-, and cloud-oriented ML workflows.",{"name":84,"@type":75,"acceptedAnswer":85},"Which machine learning model performs best and what accuracy is reported?",{"text":86,"@type":78},"The Decision Tree algorithm running on smart devices outperforms other approaches in training efficiency, resource utilization, and detection capability. 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