[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123758-en":3,"doc-seo-123758-105":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},123758,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",6,"Technology","Intrusion Detection in IoT networks using Machine Learning","The exponential expansion of Internet of Things (IoT) infrastructure creates major security risks as large numbers of interconnected devices support Industry 4.0, smart cities, and critical services. This work evaluates Machine Learning models for Intrusion Detection Systems (IDS) that monitor networks and automatically alert malicious activity. Five supervised classifiers are trained on a public IoT attacks dataset and tested across binary, 8-category, and 33-attack multiclass tasks using 10-fold cross-validation. Random Forest achieves the highest overall performance, while Gaussian Naïve Bayes delivers the fastest detection with lower accuracy, and Decision Trees balance speed and effectiveness.","MASTER THESIS  \nTITLE: Intrusion Detection in IoT networks using Machine Learning  \nMASTER DEGREE: Master's degree in Applied Telecommunications and Engineering Management (MASTEAM)  \nAUTHOR: Francisco Camilo Mejías Espinosa  \nADVISOR: Olga León Abarca  \nDATE: October 18th, 2023  \nTitle: Intrusion Detection in IoT networks using Machine Learning  \nAuthor: Francisco Camilo Mejías Espinosa  \nAdvisor: Olga León Abarca  \nDate: October 18th , 2023  \nAbstract  \nThe exponential growth of Internet of Things (IoT) infrastructure has introduced significant security challenges due to the large-scale deployment of interconnected devices. IoT devices are present in every aspect of our modern life; they are essential components of Industry 4.0 , smart cities, and critical infrastructures. Therefore, the detection of attacks on this platform becomes necessary through an Intrusion Detection Systems (IDS) . These tools are dedicated hardware devices or software that monitors a network to detect and automatically alert the presence of malicious activity.  \nThis study aimed to assess the viability of Machine Learning Models for IDS within IoT infrastructures. Five classifiers, encompassing a spectrum from linear models like Logistic Regression , Decision Trees from Trees Algorithms, Gaussian Naïve Bayes from Probabilistic models, Random Forest from ensemble family and Multi-Layer Perceptron from Artificial Neural Networks , were analysed. These models were trained using supervised methods on a public IoT attacks dataset, with three tasks ranging from binary classification (determining if a sample was part of an attack) to multiclassification of 8 groups of attack categories and the multiclassification of 33 individual attacks. Various metrics were considered, from performance to execution times and all models were trained and tuned using cross-validation of 10 k-folds.  \nOn the three classification tasks, Random Forest was found to be the model with best performance , at expenses of time consumption. Gaussian Naïve Bayes was the fastest algorithm in all classification’s tasks , but with a lower performance detecting attacks. Whereas Decision Trees shows a good balance between performance and processing speed.  \nClassifying among 8 attack categories, most models showed vulnerabilities to specific attack types, especially those in minority classes due to dataset imbalances. In more granular 33 attack type classifications, all models generally faced challenges, but Random Forest remained the most reliable, despite vulnerabilities.  \nIn conclusion, Machine Learning algorithms proves to be effective for IDS in IoT infrastructure, with Random Forest model being the most robust, but with Decision Trees offering a good balance between speed and performance.  \nI would like to thank to my Advisor Olga León Abarca,  \nfor her guidance , support and patience during the execution of this project , to my sister Amalia and niece Emily, for their hospitality towards me and my family, which made my stay in Barcelona possible.  \nAnd last but not least, to my wife Olga and my daughter Eva for their unwavering support and encouragement in the execution and completion of this Master. To both of them, I dedicate this work.  \nFrancisco Camilo.  \nCONTENTS  \nCHAPTER 1. INTRODUCTION.......................................................................... 1  \nCHAPTER 2. STATE OF THE ART ................................................................... 3  \n2.1. Internet of Things .............................................................................................................. 3  \n2.1.1. IoT Infrastructure .................................................................................................... 4  \n2.1.2. Attacks on IoT ecosystem ...................................................................................... 5  \n2.2. Intrusion Detection Systems (IDS) ................................................................................... 8  \n2.3.","cbCaiabM0AJjTiOD","https://ap.wps.com/l/cbCaiabM0AJjTiOD","pdf",4554588,1,89,"English","en",105,"# Chapter 1. Introduction\n# Chapter 2. State of the Art\n## 2.1. Internet of Things\n## 2.2. Intrusion Detection Systems (IDS)\n## 2.3. Machine Learning techniques\n# Chapter 3. Methodology\n## 3.1. IoT Dataset selection\n## 3.2. Feature extraction and reduction\n## 3.3. Model selection\n## 3.4. Performance evaluation and metrics\n# Chapter 4. Results and Discussion","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"It addresses security challenges in IoT networks by evaluating how Machine Learning can detect intrusions and support IDS-based monitoring and alerts.\"},{\"question\":\"Which Machine Learning classifiers are evaluated and how are they trained?\",\"answer\":\"Five supervised classifiers are analyzed: Logistic Regression, Decision Trees, Gaussian Naïve Bayes, Random Forest, and Multi-Layer Perceptron. They are trained on a public IoT attacks dataset and tuned using 10-fold cross-validation.\"},{\"question\":\"How do the models compare across different classification tasks?\",\"answer\":\"On binary, 8-category, and 33-attack multiclass tasks, Random Forest delivers the best overall performance but with higher time cost. Gaussian Naïve Bayes is the fastest yet has lower detection performance, while Decision Trees provide a strong balance between speed and accuracy.\"}]","Intrusion Detection in IoT networks using Machine Learning | PDF",1785818372,224,{"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},"intrusion-detection-in-iot-networks-using-machine-learning","",{"@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/intrusion-detection-in-iot-networks-using-machine-learning/123758/",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},"What problem does the thesis address?","Question",{"text":75,"@type":76},"It addresses security challenges in IoT networks by evaluating how Machine Learning can detect intrusions and support IDS-based monitoring and alerts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which Machine Learning classifiers are evaluated and how are they trained?",{"text":80,"@type":76},"Five supervised classifiers are analyzed: Logistic Regression, Decision Trees, Gaussian Naïve Bayes, Random Forest, and Multi-Layer Perceptron. They are trained on a public IoT attacks dataset and tuned using 10-fold cross-validation.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the models compare across different classification tasks?",{"text":84,"@type":76},"On binary, 8-category, and 33-attack multiclass tasks, Random Forest delivers the best overall performance but with higher time cost. Gaussian Naïve Bayes is the fastest yet has lower detection performance, while Decision Trees provide a strong balance between speed and accuracy.","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},[93,97,101,105,110,113,118,123,128,131,135],{"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":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]