[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126918-en":3,"doc-seo-126918-105":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},126918,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Identifying Internet of Things Devices through Unique Digital Signatures and Advanced Machine Learning Techniques","Rapid growth of the Internet of Things has increased the number and diversity of connected devices across many sectors, making reliable device recognition essential. Device fingerprinting—based on network behaviour, communication patterns, and hardware characteristics—enables practical recognition and classification. The proposed machine learning approach analyses device fingerprints to identify sensors, actuators, and intelligent appliances with high accuracy while detecting suspicious devices, using low computational overhead suitable for real-time deployment and validated on multiple IoT datasets.","Identifying Internet of Things Devices through Unique Digital Signaturesand Advanced Machine Learning Techniques  \nTaiwo Abdulahi Akintayo 1, Richards Obada Okiemute 2, Moyosore Celestina Owoeye 3, Oluwaseyi Sulaimon Balogun 4, Chadi Paul 5, Madumere Chiamaka Queenet 6,  \nRuth Onyekachi Okereke 7, Richie Chukwunalu Moluno 2, Adedokun Seyi Adediran 8, Christian Chukwuemeka Nzeanorue 9, Egenuka Rhoda Ngozi 6, Chika Moses Madukwe 10  \n1 National Centre of Artificial Intelligence and Robotics  \nNo 28, Port Harcourt Crescent, Off Gimbiya Street, P. M. B 564, Area 11, Garki, Abuja, Nigeria  \n2 University of Benin  \nP. M. B. 1154, Ugbowo, Benin City, Edo State, Nigeria  \n3 Federal University Oye-Ekiti  \nOye-Are Road, Oye Ekiti, Ekiti State, Nigeria  \n4 Tai Solarin University of Education  \nP.M.B. 2118, Ijagun, Ijebu Ode, Ogun State, Nigeria  \n5 The Federal University of Technology Owerri  \nP. M. B. 1526, Ihiagwa, Owerri, Nigeria  \n6 Federal Polytechnic Nekede Owerri  \nNekede Ihiagwa Road, Nekede, P. M. B. 1036, Owerri, Imo State, Nigeria  \n7 National Open University of Nigeria  \nPlot 91, Cadastral Zone, Nnamdi Azikwe Expressway, Jabi, Abuja, Nigeria  \n8 LadokeAkintola University of Technology  \nP. M. B. 4000, Ogbomoso, Oyo State, Nigeria  \n9 The George Washington University  \n1918 F Street, NW, Washington, DC 20052, USA  \n10Federal Polytechnic Idah  \nP. M. B. 1037, Idah Kogi State, Nigeria  \nDOI: 10.22178/pos.106-5  \nLCC Subject Category: T58.5-58.64  \nReceived 25.06.2024 Accepted 28.07.2024 Published online 31.07.2024  \nCorresponding Author: Taiwo Abdulahi Akintayo  \n[taiwoabdulahi15@gmail.com](taiwoabdulahi15@gmail.com)  \n© 2024 The Authors. This article is licensed under a Creative Commons Attribution 4.0 License   \nAbstract. The rapid growth of the Internet of Things (IoT) has led to a surge in connected devices across various sectors, necessitating reliable device recognition techniques. Device fingerprinting, which involves analysing network behaviour, communication patterns, and hardware features, offers a solution. Our proposed method leverages machine learning algorithms to analyse and categorise device fingerprints, achieving exceptional accuracy in identifying diverse devices, including sensors, actuators, and intelligent appliances. Moreover, it effectively detects suspicious devices and has a low computational overhead, making it suitable for real-time deployment. Our model demonstrates its effectiveness through rigorous testing and validation on multiple IoT datasets. The benefits of device fingerprinting for IoT device identification include enhanced security, improved network management, and increased visibility into device behaviour, making it a valuable tool for IoT ecosystem management.  \nKeywords: IoT; Device recognition; Machine learning; Real-Time Deployment; Device Fingerprint.  \nINTRODUCTION  \nThe Internet of Things (IoT) is a network of physical objects people call \"things\" embedded with software, electronics, networks, and sensors that allow these objects to collect and exchange data. IoT aims to extend internet connectivity from standard devices like computers, mobile, and tablets to relatively dumb devices like a toaster. IoT makes virtually everything \"smart\" by improving aspects of our lives with the power of data collection, AI algorithms, and networks. The IoT process starts with devices like smartphones, smartwatches, and electronic appliances like TVsand Washing machines, which help communicate with the IoT platform [1].  \nThe rise of the Internet of Things (IoT) has led many devices connected to networks to collect and transmit data for continuous further analysis. With advancements in deep learning, many applications now use these techniques to analyse collected data, enabling \"intelligence\" and \"automation\". Consequently, leveraging data analysis and IoT infrastructure, \"Smart Cities\" have emerged, encompassing intelligent grids, transportation, manufacturing, and buildings [2].  \nAs a rapidly evo","cbCaidDGNSoENFv4","https://ap.wps.com/l/cbCaidDGNSoENFv4","pdf",462409,1,7,"English","en",105,"# Introduction\n## IoT overview and intelligent services\n## Security threats from rogue devices\n## Device detection and identification perspectives\n# Proposed approach: device fingerprinting","[{\"question\":\"What does device fingerprinting use to recognize IoT devices?\",\"answer\":\"It analyses network behaviour, communication patterns, and hardware features to form device fingerprints for recognition.\"},{\"question\":\"How does the proposed machine learning method improve IoT device identification?\",\"answer\":\"It analyses and categorises device fingerprints, achieving high accuracy for diverse device types and supporting detection of suspicious devices.\"},{\"question\":\"Why is the approach suitable for real-time deployment?\",\"answer\":\"It is described as having low computational overhead, enabling timely identification in operational IoT environments.\"}]","Identifying Internet of Things Devices through Unique Digital Signatures and Advanced Machine Learning Techniques | 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