[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120428-en":3,"doc-seo-120428-105":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},120428,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","IoT Security Using Machine Learning Methods - Master of Science Project","Rapid growth of internet-connected devices makes robust cybersecurity essential for protecting IoT environments from evolving cyber threats. IoT-specific constraints such as limited processing capacity, minimal built-in security features, and exposure to attacks like DoS and DDoS increase the difficulty of defense. The work presents IoT security strategies including encryption, authentication, access control, and real-time threat detection using machine learning and AI. It includes two related projects: a deep-learning intrusion detection system for DDoS detection and a fingerprint-based approach for identifying network abnormalities.","IoT Security Using Machine Learning Methods  \nby  \nSeyedamiryousef Hosseini Goki  \nA Project Submitted in Partial Fulfillment of the Requirements for the Degree of  \nMaster of Science  \nin the Department of Computer Science  \n© Seyedamiryousef Hosseini Goki, 2023  \nUniversity of Victoria  \nAll rights reserved. This project may not be reproduced in whole or in part, by photocopying or other means, without the permission of the author.  \nIoT Security Using Machine Learning Methods  \nBy  \nSeyedamiryousef Hosseini Goki  \nSupervisory Committee  \nDr. Kui Wu, Supervisor  \n(Department of Computer Science)  \nDr. Jianping Pan, Departmental Member (Department of Computer Science)  \nABSTRACT  \nThe rapid growth of internet-connected devices has made robust cybersecurity measures essential to protect against cyber threats. IoT cybersecurity includes various methods and technologies to secure internet-connected devices and systems from cyber attacks. The unique nature ofIoT devices and systems poses several challenges to cybersecurity, including limited processing power, minimal security features, and vulnerability to attacks like DoS and DDoS. Cybersecurity strategies for IoT include encryption, authentication, access control, and threat detection and response, which utilize machine learning and artificial intelligence technologies to identify and respond to potential cyber attacks in real-time. The report discusses two projects related to cybersecurity in IoT environments, one focused on developing an intrusion detection system (IDS) based on deep learning algorithms to detect DDoS attacks, and another focused on identifying potential abnormalities in IoT networks using a fingerprint. These projects highlight the importance of prioritizing cybersecurity measures to protect against the growing number of cyber threats facing IoT devices and systems.  \nContents  \nSupervisory Committee-------------------------------------------------------------------------------------ii  \nAbstract------------------------------------------------------------------------------------------------ ------iii  \nList of Tables------------------------------------------------------------------------------------------------vi  \nList of Figures----------------------------------------------------------------------------------------------vii  \nAcknowledgment-------------------------------------------------------------------------------------------viii  \nChapter One-Introduction---------------------------------------------------------------- ---------------1  \n1.1 Structure of the Report---------------------------------------------------------------- ----------------4  \nChapter Two-Related Work---------------------------------------------------------------- -------------5  \n2.1 Literature Review for the first Project-------------------------------- ------------------------------5  \n2.2 Literature Review for the second Project-------------------------------- ---------------------------9  \nChapter Three-Project One-----------------------------------------------------------------------------11  \n3.1 Methods and Material--------------------------------------------------------------------------------11  \n3.1.1 Distributed Denial-of-Service (DDoS) Attack-----------------------------------------------11  \n3.1.2 Feature Extraction--------------------------------------------------------------------------------13  \n3.1.3 Multilayer Perceptron---------------------------------------------------------------------------14  \n3.1.4 Long Short-Term Memory (LSTM)-----------------------------------------------------------15  \n3.2 Results and Discussion-------------------------------------------------------------------------------16  \n3.2.1 Data Collection-----------------------------------------------------------------------------------16  \n3.2.2 Results of Feature Extraction-------------------------------------------------------------------17  \n3.2.3 Classification Results---------------------","cbCaimL2GoVLGu6R","https://ap.wps.com/l/cbCaimL2GoVLGu6R","pdf",1194802,1,58,"English","en",105,"# Chapter One - Introduction\n## Structure of the Report\n# Chapter Two - Related Work\n## Literature Review for the first Project\n## Literature Review for the second Project\n# Chapter Three - Project One\n## Methods and Material\n## Results and Discussion\n# Chapter Four - Project Two\n## Methods and Materials\n## Results and Discussion\n# Chapter Five - Conclusion and Future Work\n# References","[{\"question\":\"Why is IoT cybersecurity more challenging than traditional cybersecurity?\",\"answer\":\"IoT systems face constraints such as limited processing power and minimal security features, and they are vulnerable to attacks including DoS and DDoS, which complicates defense.\"},{\"question\":\"What is Project One focused on?\",\"answer\":\"Project One develops an intrusion detection system using deep learning methods to detect DDoS attacks in IoT environments.\"},{\"question\":\"How does Project Two identify abnormalities in IoT networks?\",\"answer\":\"Project Two uses machine learning techniques, including convolutional neural networks, and proposes a fingerprint-based approach combined with performance evaluation metrics to detect abnormal behavior.\"}]","IoT Security Using Machine Learning Methods - 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