[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123574-en":3,"doc-seo-123574-105":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},123574,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Engineering the Application of Machine Learning in an IDS Based on IoT Traffic Flow - read online free","Internet of Things (IoT) devices underpin smart-city services, but their limited CPU, storage, and memory make them attractive targets for attackers who exploit connected networks and generate large volumes of sensitive data. This work combines machine learning with an Intrusion Detection System (IDS) to identify malicious traffic within IoT data flows, requiring a specialized, distributed architecture due to device constraints and low-latency needs. A fog-computing-based proposal uses deep neural networks, compares against three architectures, and evaluates generalization across datasets using Recall, Precision, and F1-Score.","Intelligent Systems with Applications 17 (2023) 200189  \nContents lists available at ScienceDirect  \nIntelligent Systems with Applications  \njournal [homepage: www.journals.elsevier.com/intelligent-systems-with-applications](homepage: www.journals.elsevier.com/intelligent-systems-with-applications)  \n| Engineering the application of machine learning in an IDS based on IoTtraffic flow |  |  |  |\n| --- | --- | --- | --- |\n| Nuno Prazeres a, Rogério Luís de C. Costa b,∗ , Leonel Santos a,b, Carlos Rabadão a,b\u003Cbr>a School of Technology and Management (ESTG), Polytechnic of Leiria, Leiria, 2411-901, Portugal\u003Cbr>b Computer Science and Communication Research Centre (CIIC), Polytechnic of Leiria, Leiria, 2411-901, Portugal |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Intrusion detection systems Internet of things Machine learning\u003Cbr>Smart city\u003Cbr>Cybersecurity |  | Internet of Things (IoT) devices are now widely used, enabling intelligent services that, in association with new communication technologies like the 5G and broadband internet, boost smart-city environments. Despite their limited resources, IoT devices collect and share large amounts of data and are connected to the internet, becoming an attractive target for malicious actors.\u003Cbr>This work uses machine learning combined with an Intrusion Detection System (IDS) to detect possible attacks. Due to the limitations of IoT devices and low latency services, the IDS must have a specialized architecture. Furthermore, although machine learning-based solutions have high potential, there are still challenges related to training and generalization, which may impose constraints on the architecture.\u003Cbr>Our proposal is an IDS with a distributed architecture that relies on Fog computing to run specialized modulesand use deep neural networks to identify malicious traffic inside IoT data flows. We compare our IoT-Flow IDS with three other architectures. We assess model generalization using test data from different datasets and evaluate their performance in terms of Recall, Precision, and F1-Score. Results confirm the feasibility of flowbased anomaly detection and the importance of network traffic segmentation and specialized models in the AI-based IDS for IoT. |  |\n\n1. Introduction  \nThe Internet of Things (IoT) paradigm is one of the drivers for a new generation of communication networks, combining a wide variety of hardware and software that provide customers easy-to-use experience and low-cost solutions. IoT devices are present in our cities, homes, industry, healthcare facilities, vehicles, and personal gadgets and can perform several critical tasks and make our lives more comfortable (Figueiredo et al., 2022, Tewari & Gupta, 2017). IoT networks are made up of a large number of devices and sensors that collect and share large volumes of data, including confidential and private ones (Neisse et al., 2014, Tewari & Gupta, 2020). The use of IoT devices has also boosted the creation of smart environments, such as smart cities. In these contexts, they provide a wide variety of services, increasing the well-being of the population and the conscious use of resources (Figueiredo et al., 2022).  \nDespite handling and generating a large volume of data, IoT devices are generally cheap and have low CPU capacity, low storage, and low  \nmemory resources. IoT devices may be vulnerable to attacks if there are no security measures, producing unexpected behaviors in the private networks, compromising services availability, data confidentiality, and the user’s privacy (Butun et al., 2019, Hromada et al., 2021). Hence, the IoT ecosystem is a potential target for cybercriminals and requires novel solutions to deal with data protection and cybersecurity threats (Hromada et al., 2021, Tsimenidis et al., 2022).  \nIntrusion Detection Systems (IDS) are key security solutions for networks as they may detect non-authorized accesses and attacks against systems through the analyses of network communic","cbCaid3iReBAjgUW","https://ap.wps.com/l/cbCaid3iReBAjgUW","pdf",2190769,1,13,"English","en",105,"# Introduction\n## IoT and smart-city security challenges\n## Role of intrusion detection systems and AI/ML\n## Paper contribution and IDS design overview","[{\"question\":\"Why are IoT devices a cybersecurity risk in smart cities?\",\"answer\":\"IoT devices collect and share large volumes of data while having limited compute, storage, and memory, which increases vulnerability. Without adequate security measures, attackers can compromise availability, confidentiality, and user privacy.\"},{\"question\":\"How does the proposed IDS detect attacks in IoT traffic flows?\",\"answer\":\"The work uses machine learning integrated with an IDS and deploys a distributed architecture supported by fog computing. Specialized modules run deep neural networks to identify malicious traffic within IoT data flows.\"},{\"question\":\"How is the model performance and generalization evaluated?\",\"answer\":\"The study compares the IoT-Flow IDS with three other architectures. It assesses generalization using test data from different datasets and reports metrics including Recall, Precision, and F1-Score.\"}]","Engineering the Application of Machine Learning in an IDS Based on IoT Traffic Flow - read online free | PDF",1785817418,33,{"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},"engineering-the-application-of-machine-learning-in-an-ids-based-on-iot-traffic-flow-read-online-free","",{"@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/engineering-the-application-of-machine-learning-in-an-ids-based-on-iot-traffic-flow-read-online-free/123574/",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 are IoT devices a cybersecurity risk in smart cities?","Question",{"text":75,"@type":76},"IoT devices collect and share large volumes of data while having limited compute, storage, and memory, which increases vulnerability. Without adequate security measures, attackers can compromise availability, confidentiality, and user privacy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed IDS detect attacks in IoT traffic flows?",{"text":80,"@type":76},"The work uses machine learning integrated with an IDS and deploys a distributed architecture supported by fog computing. Specialized modules run deep neural networks to identify malicious traffic within IoT data flows.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the model performance and generalization evaluated?",{"text":84,"@type":76},"The study compares the IoT-Flow IDS with three other architectures. 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