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This report studies a DJI Edu Tello drone by assessing vulnerabilities and building anomaly detection mechanisms with unsupervised machine learning. Benign and attack datasets covering nine attack types are collected, while feature extraction is adapted from CIC scripts to improve robustness by revising/removing features and adding packet-based measurements. Isolation Forest, LOF, and Elliptic Envelope are compared through performance evaluations, showing strong anomaly detection effectiveness and underscoring the importance of robust feature engineering and careful algorithm selection for UAV security.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/anomaly-detection-in-drone-activities-data-collection-and-unsupervised-machine-learning-modeling/291094/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/anomaly-detection-in-drone-activities-data-collection-and-unsupervised-machine-learning-modeling/291094.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What datasets are used for anomaly detection in the study?","Question",{"text":113,"@type":114},"The study collects benign data from legitimate drone actions and attack data comprising nine types of attacks.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How are features engineered for the anomaly detector?",{"text":118,"@type":114},"Feature extraction and engineering are adapted from Canadian Institute for Cybersecurity (CIC) scripts by removing/modifying existing features and introducing new packet-based measurements to improve robustness.",{"name":120,"@type":111,"acceptedAnswer":121},"Which unsupervised algorithms are compared, and what is the outcome?",{"text":122,"@type":114},"Isolation Forest, Local Outlier Factor (LOF), and Elliptic Envelope are compared using extensive performance evaluations, and the results show effective anomaly detection for improving drone security.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291094,1789643434,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":26,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Anomaly Detection in Drone Activities: Data Collection and Unsupervised Machine  \nLearning Modeling  \nby  \nZhuo Chen  \nB.Eng, University of Regina, Regina, 2023  \nA Report Submitted in Partial Fulfillment of the Requirements for the Degree of  \nMASTER OF ENGINEERING  \nin the Department of Electrical and Computer Engineering  \n􀂤 Zhuo Chen, 2024 University of Victoria  \nAll rights reserved. This report may not be reproduced in whole or in part, by photocopy or other means, without the permission of the author.  \nii  \nSupervisory Committee  \nComprehensive Study on Anomaly Detection in Drone Data: Data Collection, Feature Extraction, Sets Selection, and Model Performance  \nEvaluation  \nby  \nZhuo Chen  \nB.Eng, University of Regina, Regina, 2023  \nSupervisory Committee  \nDr. Issa Traoré, Supervisor  \n(Department of Electrical and Computer Engineering)  \nDr. Mohammad. Mamun, Co-Supervisor  \n(Department of Electrical and Computer Engineering)  \niii  \nAbstract  \nAs Internet of Things (IoT) devices, drones are among the most popular unmanned aerial vehicles (UAVs), equipped with multiple sensors, cameras, and communication systems. These features expose them to potential vulnerabilities exploitable by hackers. making it crucial to explore these vulnerabilities and implement effective anomaly detection while operating UAVs. This study investigates a DJI Edu Tello drone to comprehensively assess its vulnerabilities and develop anomaly detection mechanisms using different unsupervised machine learning techniques. Two types of data were collected: benign data from legitimate actions and attack data comprising nine types of attacks. Feature extraction and engineering were performed based on scripts from the Canadian Institute for Cybersecurity (CIC), which were modified to suit the specific needs of this project. The modifications aimed to improve the robustness of the detector by removing and modifying existing features and introducing new measurements to represent the captured packets. The anomaly detector was formulated after comparing three unsupervised machine learning algorithms: Isolation Forest, Local Outlier Factor (LOF), and Elliptic Envelope, through extensive performance evaluations and analyses. The study demonstrated the effectiveness of these algorithms in detecting anomalies and enhancing the security of drones. The findings also highlight the critical role of robust feature engineering and careful algorithm selection in developing a reliable anomaly detection system for UAVs.  \niv  \nTable of Contents  \nSupervisory Committee ...................................................................................................... ii  \nAbstract .............................................................................................................................. iii  \nTable of Contents ............................................................................................................... iv  \n[List of Tables ..................................................................................................................... vi](List of Tables ..................................................................................................................... vi)  \n[List of Figures ................................................................................................................... vii](List of Figures ................................................................................................................... vii)  \n[Glossary ........................................................................................................................... viii](Glossary ........................................................................................................................... viii)  \n[Acknowledgments..............................................................................................................](Acknowledgments.................................................................","cbCaicyKDNZYaHpc","https://ap.wps.com/l/cbCaicyKDNZYaHpc","pdf",2377438,"English","# Chapter 1: Introduction\n## 1.1 Context\n## 1.2 Related Work\n## 1.3 Objective\n## 1.4 Report Outline\n# Chapter 2: Background and Model Architecture\n## 2.1 Basics\n## 2.2 Classification of Communications\n## 2.3 Characteristics of Communications\n## 2.4 Vulnerability Analysis of the Drone\n## 2.5 Potential Risks of Cybersecurity Vulnerabilities\n## 2.6 Anomaly Detection System Overview\n# Chapter 3: Data Collection\n## 3.1 Attack Vectors\n## 3.1.1 Password Cracking","[{\"question\":\"What datasets are used for anomaly detection in the study?\",\"answer\":\"The study collects benign data from legitimate drone actions and attack data comprising nine types of attacks.\"},{\"question\":\"How are features engineered for the anomaly detector?\",\"answer\":\"Feature extraction and engineering are adapted from Canadian Institute for Cybersecurity (CIC) scripts by removing/modifying existing features and introducing new packet-based measurements to improve robustness.\"},{\"question\":\"Which unsupervised algorithms are compared, and what is the outcome?\",\"answer\":\"Isolation Forest, Local Outlier Factor (LOF), and Elliptic Envelope are compared using extensive performance evaluations, and the results show effective anomaly detection for improving drone security.\"}]","Anomaly Detection in Drone Activities - Data Collection and Unsupervised Machine Learning Modeling | PDF",151]