[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122904-en":3,"doc-seo-122904-105":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},122904,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Implementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes - Research Report","Smart home IoT devices provide convenience while expanding the attack surface for threats to homeowners’ security and privacy. Limited computational resources make deploying effective intrusion detection especially challenging. This work proposes a two-layer, lightweight machine learning intrusion detection system, with edge deployment on a microcontroller-based smart thermostat and cloud-side classification for enhanced accuracy and efficiency. The approach achieves 99.50% accuracy at the cloud multiclass level. For real-time evaluation, a Raspberry Pi 4 adversary generates MITM and DoS datasets, and an XGBoost-based model detects both attacks on the thermostat in 3.51 ms with 97.59% accuracy.","Implementation of lightweight machine learning-based intrusion detection system on IoT devices of smart homes  \nJaved, Abbas; Ehtsham, Amna; Jawad, Muhammad; Awais, Muhammad Naeem; Qureshi, Ayyaz-ul-Haq; Larijani, Hadi  \nPublished in:  \nFuture Internet  \nDOI:  \n10.3390/fi16060200  \nPublication date:  \n2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in ResearchOnline  \nCitation for published version (Harvard):  \nJaved, A, Ehtsham, A, Jawad, M, Awais, MN, Qureshi, A-H & Larijani, H 2024, ' Implementation of lightweight machine learning-based intrusion detection system on IoT devices of smart homes', Future Internet, vol. 16, no.  \n6, 200. 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Jul. 2024  \nfuture internet  \nArticle  \nImplementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes  \nAbbas Javed 1, Amna Ehtsham 1, Muhammad Jawad 1,2, Muhammad Naeem Awais 1, Ayyaz-ul-Haq Qureshi 3, * and Hadi Larijani 4  \nCitation: Javed, A.; Ehtsham, A.; Jawad, M.; Awais, M.N.; Qureshi, A.-u.-H.; Larijani, H. Implementation of Lightweight Machine  \nLearning-Based Intrusion Detection System on IoT Devices of Smart Homes. Future Internet 2024, 16, 200 . [https://doi.org/10.3390/fi16060200](https://doi.org/10.3390/fi16060200)  \nAcademic Editors: Olivier  \nMarkowitch and Jean-Michel Dricot  \nReceived: 1 May 2024  \nRevised: 30 May 2024  \nAccepted: 1 June 2024  \nPublished: 5 June 2024  \nCopyright: © 2024 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 Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Punjab 54000, Pakistan; [abbasjaved@cuilahore.edu.pk](abbasjaved@cuilahore.edu.pk) (A.J.);  \n[amna.ehtsham@gmail.com](amna.ehtsham@gmail.com) (A.E.); [mjawad@cuilahore.edu.pk](mjawad@cuilahore.edu.pk) (M.J.); [naeem.awais@cuilahore.edu.pk](naeem.awais@cuilahore.edu.pk) (M.N.A.)  \n2 Hitachi Energy Research, Pawia 7, 31-154 Kraków, Poland  \n3 Department of Cyber Security and Networks, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK  \n4 SMART Technology Research Centre, Department of Cyber Security and Networks, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK; [h.larijani@gcu.ac.uk](h.larijani@gcu.ac.uk)  \n* Correspondence: [aqu2@gcu.ac.uk](aqu2@gcu.ac.uk)  \nAbstract: Smart home devices, also known as IoT devices, provide significant convenience; however, they also present opportunities for attackers to jeopardize homeowners’ security and privacy. Securing these IoT devices is a formidable challenge because of their limited computational resources. Machine learning-based intrusion detection systems (IDSs) have been implemented on the edge and the cloud; however, IDSs have not been embedded in IoT devices. To address this, we propose a novel machine learning-based two-layered IDS for smart home IoT devices, enhancing accuracy and computational efficiency. The ","cbCaihG0p9I16L23","https://ap.wps.com/l/cbCaihG0p9I16L23","pdf",3022965,1,23,"English","en",105,"# Introduction\n## Threats to Smart Home IoT Security\n# Proposed Two-Layer IDS Approach\n## Edge Layer on Smart Thermostat\n## Cloud Layer for Attack Classification\n# Experimental Setup and Results\n## Dataset Generation for MITM and DoS\n## Detection Accuracy and Runtime Performance","[{\"question\":\"Why is intrusion detection difficult on smart home IoT devices?\",\"answer\":\"Because IoT devices have limited computational resources, making it hard to run effective IDS models directly on the edge.\"},{\"question\":\"How does the proposed two-layer intrusion detection system work?\",\"answer\":\"The first layer runs on a microcontroller-based smart thermostat and uploads data to a cloud website; the second layer runs on the cloud to classify attacks.\"},{\"question\":\"What performance was achieved for attack detection in real-time testing?\",\"answer\":\"Using an XGBoost-based IDS, MITM and DoS attacks were detected in 3.51 ms on a smart thermostat with 97.59% accuracy.\"}]","Implementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes - Research Report | PDF",1785813588,58,{"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},"implementation-of-lightweight-machine-learning-based-intrusion-detection-system-on-iot-devices-of-smart-homes-research-report","",{"@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/implementation-of-lightweight-machine-learning-based-intrusion-detection-system-on-iot-devices-of-smart-homes-research-report/122904/",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","2026-08-04",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},"Why is intrusion detection difficult on smart home IoT devices?","Question",{"text":75,"@type":76},"Because IoT devices have limited computational resources, making it hard to run effective IDS models directly on the edge.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed two-layer intrusion detection system work?",{"text":80,"@type":76},"The first layer runs on a microcontroller-based smart thermostat and uploads data to a cloud website; 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