[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123028-en":3,"doc-seo-123028-105":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},123028,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","INTELLIGENT HOME IOT DEVICES - AN EXPLORATION OF MACHINE LEARNING-BASED NETWORKED TRAFFIC INVESTIGATION","Smart homes powered by Internet of Things (IoT) devices demand strong safety and privacy, yet pervasive security vulnerabilities and heterogeneous QoS requirements make unified network management impractical. The study proposes IoT device categorization to identify rogue or vulnerable devices and automate network operations by device type or function, strengthening security while simplifying management. It surveys machine-learning methods for traffic analysis across the workflow, including dataset categorization, traffic data collection practices, and feature extraction techniques. Rigorous algorithm evaluation produces taxonomies and highlights future research directions for IoT security and management.","INTELLIGENT HOME IOT DEVICES: AN EXPLORATION OF MACHINE LEARNING-BASED NETWORKED TRAFFIC INVESTIGATION  \nSaman M. Almufti 1, Ahmed Alaa Hani 1, Subhi R. M. Zeebaree2, Renas Rajab Asaad 1,*, Dilovan  \nAsaad Majeed 1, Amira Bibo Sallow3, Hawar Bahzad Ahmad 1  \n1 Department of Computer Science, Nawroz University, Duhok, Iraq  \n2 Energy Engineering Department, Duhok Polytechnic University, Duhok, Iraq  \n3 Department of Information Technology, Duhok Polytechnic University, Duhok, Iraq Corresponding author email: [renas.rekany@nawroz.edu.krd](renas.rekany@nawroz.edu.krd)  \nArticle Info  \nRecieved: Mar 02, 2024  \nRevised: Apr 12, 2024  \nAccepted: May 01, 2024  \nOnlineVersion: May 14, 2024  \nAbstract  \nIn the rapidly evolving landscape of smart homes powered by Internet of Things (IoT) devices, the twin specters of safety and privacy loom large, exacerbated by pervasive security vulnerabilities. Confronted with a heterogeneous array of devices each with unique Value of Service (QoS) requirements, devising a singular network management strategy proves untenable. To mitigate these risks, device categorization emerges as a promising avenue, wherein rogue or vulnerable devices are identified and network operations are automated based on device type or function. This novel approach not only fortifies IoT security but also streamlines network management, offering a multifaceted solution to the burgeoning challenges. Recognizing the burgeoning interest in leveraging machine learning for traffic analysis in IoT environments, this study delves deep into the potential and pitfalls of such techniques. Beginning with a comprehensive framework for categorizing IoT devices, the research meticulously examines methodologies and remedies across every stage of the workflow. Key focal points include the categorization of public datasets, nuanced analysis of IoT traffic data collection methodologies, and the exploration of feature extraction techniques. Through a rigorous evaluation of machine learning algorithms for IoT device classification, the study elucidates emerging trends and highlights promising avenues for future exploration. The culmination of this investigation manifests in meticulously crafted taxonomies, offering insights into prevailing patterns and informing future research trajectories. Moreover, the study identifies and advocates for uncharted territories within this burgeoning domain, propelling the discourse forward and catalyzing innovation in IoT security and management.  \nKeywords: IoT Device Classification, Machine Learning, Network Traffic Analysis, Smart Home  \n© 2024 by the author(s)  \nThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nINTRODUCTION  \nThe Web of Things (IoT) has grown significantly over the last ten years; estimates indicate that by 2022, there will be 14.4 billion active connections, an 18% increase. IoT is defined differently by different people, but most define it as a network that includes cameras, mobile devices, industrial machines, sensors, and other devices that are linked to each other. IoT is used in smart environments to provide consumers more control and awareness of their surroundings (Alrawi et al., 2019; Abdulqadir et al., 2021; Asaad, 2021) .  \nEven with its many advantages, the spread ofIoT raises serious security and privacy issues. The three Privacy, Security, and Performance are often given priority by IoT device makers above security, which results in poor design and susceptible equipment. Inadequately protected Internet of Things devices are appealing targets for hackers who are looking for sensitive data and unauthorised access, as shown by the cases in which smart TVs were used as listening devices (Dong et al., 2020; Ma et al., 2020; Cvitić et al., 2021; Maulud et al., 2021) . By inserting malicious data, hack","cbCaiqcHJs4s8kyx","https://ap.wps.com/l/cbCaiqcHJs4s8kyx","pdf",270735,1,10,"English","en",105,"# Introduction\n## IoT growth and device ecosystem\n## Security and privacy challenges\n## Limits of one-size-fits-all network management\n## Traffic classification vs. intrusion detection\n## Role of machine learning in traffic analysis\n# System overview\n## System flowchart","[{\"question\":\"Why is one-size-fits-all network management inadequate for IoT smart homes?\",\"answer\":\"IoT devices have heterogeneous QoS requirements, so preset rules must differ by device class. Device classification enables automation with class-specific policies.\"},{\"question\":\"How does the study distinguish traffic classification from intrusion detection and device fingerprinting?\",\"answer\":\"Intrusion detection uses attack patterns to decide whether traffic is malicious. Traffic classification groups traffic using multiple criteria, while device fingerprinting provides each device instance a unique fingerprint for identification.\"},{\"question\":\"What machine-learning-related aspects does the research examine for IoT device classification?\",\"answer\":\"It reviews methodologies across the workflow, including categorizing public datasets, analyzing traffic data collection approaches, applying feature extraction techniques, and evaluating machine learning algorithms to determine classification performance.\"}]","INTELLIGENT HOME IOT DEVICES - AN EXPLORATION OF MACHINE LEARNING-BASED NETWORKED TRAFFIC INVESTIGATION | PDF",1785814246,25,{"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},"intelligent-home-iot-devices-an-exploration-of-machine-learning-based-networked-traffic-investigation","",{"@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/intelligent-home-iot-devices-an-exploration-of-machine-learning-based-networked-traffic-investigation/123028/",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 one-size-fits-all network management inadequate for IoT smart homes?","Question",{"text":75,"@type":76},"IoT devices have heterogeneous QoS requirements, so preset rules must differ by device class. Device classification enables automation with class-specific policies.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study distinguish traffic classification from intrusion detection and device fingerprinting?",{"text":80,"@type":76},"Intrusion detection uses attack patterns to decide whether traffic is malicious. Traffic classification groups traffic using multiple criteria, while device fingerprinting provides each device instance a unique fingerprint for identification.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine-learning-related aspects does the research examine for IoT device classification?",{"text":84,"@type":76},"It reviews methodologies across the workflow, including categorizing public datasets, analyzing traffic data collection approaches, applying feature extraction techniques, and evaluating machine learning algorithms to determine classification performance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]