[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117886-en":3,"doc-seo-117886-105":30,"detail-sidebar-cat-0-en-105":92},{"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},117886,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Anomaly Detection for IoT Networks Using Machine Learning","The Internet of Things (IoT) underpins logistics tracking, healthcare, automotive systems, and smart cities, but expanding connectivity also attracts escalating cyber-attacks targeting IoT-enabled networks. This thesis improves IoT network security by advancing machine-learning-based anomaly detection, analyzing key challenges such as network scale, device count, human factors, and IoT complexity. It also addresses research gaps in signature-based intrusion detection, missing modelling of input parameters, and limited algorithm comparisons on standard versus real datasets. Experiments evaluate multiple algorithms, reduce feature sets to five, and test an enhanced CNN model (CNNwGFC) achieving improved anomaly classification accuracy.","Anomaly Detection for IoT Networks Using  \nMachine Learning  \nBy:  \nHUSAIN ABDULLA  \nA Thesis Submitted  \nin Partial Fulfilment of the Requirements for the Degree of  \nDOCTOR OF PHILOSOPHY  \nDepartment of Computer Science College of Engineering, Design and Physical Sciences  \nBRUNEL UNIVERSITY LONDON  \nJuly 2023  \nAbstract  \nThe Internet of Things (IoT) is considered one of the trending technologies today. IoT affects various industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyber-attacks and breaches are rapidly targeting networks equipped with IoT devices. This thesis aims to improve security in IoT networks by enhancing anomaly detection using machine learning.  \nThis thesis identified the challenges and gaps related to securing the Internet of Things networks. The challenges are network size, the number of devices, the human factor, and the complexity of IoT networks. The gaps identified include the lack of research on signature-based intrusion detection systems used for anomaly detection, in addition to the lack of modelling input parameters required for anomaly detection in IoT networks. Furthermore, there is a lack of comparison of the performance of machine learning algorithms on standard and real IoT datasets.  \nThis thesis creates a dataset to test the anomaly binary classification performance of the Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees machine learning algorithms and compares their results with the KDDCUP99 dataset. The results show that Support Vector Machine and Gaussian Naive Bayes perform lower than the other models on the created IoT dataset. This thesis reduces the number of features required by machine learning algorithms for anomaly detection in the IoT networks to five features only, which resulted in reduced execution time by an average of 58% .  \nThis thesis tests CNNwGFC, which is an enhanced Convolutional Neural Network model, in detecting and classifying anomalies in IoT networks. This model achieves an increase of 15.34% in the accuracy for IoT anomaly classification in the UNSW-NB15 compared to the classic Convolutional Neural Network. The CNNwGFC multi-classification accuracy (96.24%) is higher by 7.16 than the highest from the literature.  \nKeywords: IoT; Machine Learning; Security; Anomaly Detection  \nDedication  \nI dedicate this dissertation to all people who supported me throughout my educational years, especially …  \nto my mother and father for their words of support and encouragement;  \nto family members for their inspirational words;  \nto friends for instilling the importance of hard work and higher education;  \nto mentors and tutors for their efforts in mentoring and tutoring me.  \nDeclaration  \nI confirm that this thesis is my original work and is being submitted to the Post-Graduate Research Office for the first time. The research, writing, and review of this thesis were conducted by me, with guidance and supervision from my supervisors in the Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University London UK. All information obtained from other sources has been appropriately cited and acknowledged.  \nHusain Abdulla  \nJanuary 2023  \nAcknowledgements  \nI would like to extend my sincere gratitude to my supervisors, without whose guidance, support, and encouragement, this thesis would not have been possible:  \nProf. Hamed Al-Raweshidy  \nDr. Wasan Shakir Awad  \nDissertation Principal Supervisor, Brunel University  \nDissertation Supervisor, Ahlia University  \nI am deeply grateful to all of these individuals, as my dissertation would not have been successful without their support and willingness to help. My parents, brothers, and sister have always believed in my ability to succeed and have provided me with everything I needed to get to where I am today. My wife has been an endless source of support and motivation throughout this proce","cbCaij3XFSXih5FC","https://ap.wps.com/l/cbCaij3XFSXih5FC","pdf",2122144,1,147,"English","en",105,"# 1. Chapter One: Introduction\n## 1.1 Background\n## 1.2 Motivation\n## 1.3 Aim and Objectives\n## 1.4 Contributions\n## 1.5 Thesis Outline\n## 1.6 List of Publications\n# 2. Chapter Two: Related Work and Concepts\n## 2.1 Overview\n## 2.2 Machine Learning\n## 2.2.1 Overview of Machine Learning\n## 2.2.2 Artificial Neural Networks\n## 2.2.3 Decision Tree\n## 2.2.4 Support Vector Machines","[{\"question\":\"What problem does the thesis focus on for IoT networks?\",\"answer\":\"It targets improving security by enhancing anomaly detection for networks equipped with IoT devices, which are increasingly targeted by cyber-attacks and breaches.\"},{\"question\":\"Which machine learning algorithms are evaluated in the thesis?\",\"answer\":\"The thesis tests Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees, comparing results with the KDDCUP99 dataset.\"},{\"question\":\"How does the thesis improve model efficiency and classification performance?\",\"answer\":\"It reduces required features to five, cutting average execution time by 58%. It also tests the CNNwGFC enhanced convolutional neural network, improving IoT anomaly classification accuracy on UNSW-NB15.\"}]","Anomaly Detection for IoT Networks Using Machine Learning | PDF",1785680175,370,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"anomaly-detection-for-iot-networks-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/anomaly-detection-for-iot-networks-using-machine-learning/117886/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis focus on for IoT networks?","Question",{"text":76,"@type":77},"It targets improving security by enhancing anomaly detection for networks equipped with IoT devices, which are increasingly targeted by cyber-attacks and breaches.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms are evaluated in the thesis?",{"text":81,"@type":77},"The thesis tests Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees, comparing results with the KDDCUP99 dataset.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the thesis improve model efficiency and classification performance?",{"text":85,"@type":77},"It reduces required features to five, cutting average execution time by 58%. 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