[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124796-en":3,"doc-seo-124796-105":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":20,"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},124796,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",6,"Technology","Denial of Service Detection for IoT Networks Using MachineLearning","The Internet of Things (IoT) integrates connected devices into critical industries such as logistics tracking, healthcare, automotive systems, and smart cities, yet its resource-constrained nature makes it highly vulnerable to cyber-attacks. Denial-of-Service (DoS) attacks are a common threat that disrupts IoT networks, motivating automatic detection techniques. This paper evaluates neural networks, Gaussian Naive Bayes, decision trees, and support vector machines using packet analysis at regular time intervals, achieving 98% intrusion detection accuracy with a six-attribute approach that reduces training time by 58% on average. Results indicate DT and NN perform best, while NB and SVM are less effective; middle boxes with embedded ML can detect attacks in environments such as homes, manufacturers, and plants.","# Denial of Service Detection for IoT Networks Using MachineLearning\n\nHusain Abdulla¹D,Hamed S.Al-Raweshidy²D and Wasan Awad³DC  \n¹Department of Computer Science,Brunel University,Uxbridge,U.K.²Department of Electronic and Computer Engineering,Uxbridge,U.K.3Information Technology College,Ahlia University,Al-Hoora,Bahrain  \nKeywords:  Intrusion Detection System,IoT,Machine Learning,Security,Anomaly Detection.  \nAbstract:The Internet of Things (IoT)is considered one of the trending technologies today.IoT affects a variety ofindustries,including logistics tracking,healthcare,automotive and smart cities.A rising number of cyber-attacks and breaches are rapidly targeting networks equipped with IoT devices.Due to the resource-constrained nature of the IoT devices,one of the Internet security issues impacting IoT devices is the Denial-of-Service(DoS).This encourages the development of new techniques for automatically detecting DoS inIoT networks.In this paper,we test the performance of the following Machine Learning(ML)algorithms indetecting IoT DoS attacks using packet analysis at regular time intervals:Neural Networks(NN),GaussianNaive Bayes(NB),Decision Trees (DT),and Support Vector Machine(SVM).We were able to achieve 98%accuracy in intrusion detection for IoT devices.We have created a novel way of detecting the attacks usingonly six attributes,which significantly reduces the time to train the ML Models by 58%on average.Thisresearch is based on data collected from actual IoT attacks on IoT networks.This paper shows that using theDT or NN;we can detect attacks on IoT devices.Furthermore,it shows that NB and SVM are poor in detectingIoT attacks.In addition,it proves that middle boxes embedded with ML Models can be utilized to detectattacks in places such as houses,manufactures,and plants.  \n(Fadul,Reising,Loveless &Ofoli,2021)and theywill be collecting data of more than 180 zettabytes.Yet,there are plenty of these IoT devices that areinsecure and prone to attacks(Davis,Mason,&Anwar,2020).A recent security review of IoTdevices categorize these attacks into four categoriesnamely:physical,network,software,and encryptionattacks(Andrea,Chrysostomou &Hadjichristofi,2015).  \n## 1 INTRODUCTION\n\nThe Internet of Things promises an optimistictechnological future where the physical world isintegrated with computer-based systems,resulting ineconomic benefits and improvements in efficiency.The IoT is a network of objects,including devices,home appliances,and vehicles,which may beembedded with electronics,sensors,and software toenable it to connect and exchange data.Although theIoT makes considerable progress,they are vulnerableto cyberattacks due to their resource-constrainednature.Therefore,they rely on external systems,suchas intrusion detection systems,to be protected.DoSattacks are common effective attacks to disturb IoTnetworks.  \nIntrusion Detection System (IDS)is used toprevent the DoS attacks.Apart from the most usedmethod that is based on the port number,which issuited for the rule-based attack detection,machinelearning methods are widely used in recent years forDoS and anomaly detection.A recent research foranomaly detection has shown the possibility ofmachine learning to identify malicious Internet traffic(Bagaa,Taleb,Bernabe &Skarmeta,2020).  \nIt is estimated that the number of Internet ofThings (IoT)devices will be over 75 billion by 2025  \naD https://orcid.org/0000-0002-3022-1985  \nbDhttps://orcid.org/0000-0002-3702-8192  \ncD https://orcid.org/0000-0001-7152-3480  \n996  \nHowever,limited research has been done to developmachine learning models with characteristicsspecifically targeted at IoT device networks andattack traffic.The IoT devices'traffic is different fromother devices connected to the Internet(such aslaptops and mobile phones)(Mishra,Varadharajan,Tupakula &Pilli,2019).IoT devices,for example,are often connected to a small number of serviceendpoints rather than a large number of servers.Furthermore,IoT devices often genera","cbCaiacXVB21EVej","https://ap.wps.com/l/cbCaiacXVB21EVej","pdf",504875,1,8,"English","en",105,"# 1 INTRODUCTION\n## Intrusion Detection and DoS in IoT\n## Machine Learning for anomaly and DoS detection\n# 2 RELATED W","[{\"question\":\"Why are DoS attacks especially concerning for IoT networks?\",\"answer\":\"IoT devices are resource-constrained, which increases vulnerability. DoS attacks effectively disturb IoT network operation, motivating dedicated detection methods.\"},{\"question\":\"Which machine learning algorithms were evaluated for detecting IoT DoS attacks?\",\"answer\":\"The study tests neural networks, Gaussian Naive Bayes, decision trees, and support vector machine models using packet analysis at regular time intervals.\"},{\"question\":\"How does the six-attribute approach affect training and detection performance?\",\"answer\":\"Using only six attributes reduces training time by 58% on average while maintaining comparable performance, with 98% accuracy reported for intrusion detection on IoT devices.\"}]","Denial of Service Detection for IoT Networks Using MachineLearning | PDF",1785894710,20,{"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},"denial-of-service-detection-for-iot-networks-using-machinelearning","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/denial-of-service-detection-for-iot-networks-using-machinelearning/124796/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are DoS attacks especially concerning for IoT networks?","Question",{"text":75,"@type":76},"IoT devices are resource-constrained, which increases vulnerability. DoS attacks effectively disturb IoT network operation, motivating dedicated detection methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms were evaluated for detecting IoT DoS attacks?",{"text":80,"@type":76},"The study tests neural networks, Gaussian Naive Bayes, decision trees, and support vector machine models using packet analysis at regular time intervals.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the six-attribute approach affect training and detection performance?",{"text":84,"@type":76},"Using only six attributes reduces training time by 58% on average while maintaining comparable performance, with 98% accuracy reported for intrusion detection on IoT devices.","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},[93,97,101,105,110,113,118,122,126,129,133],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":29,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":29,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]