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A large dataset was collected via vulnerability analysis and testing, including about 11.5 million network traffic samples and 1.25 million MAVLink messages covering normal behavior and multiple attack and sensor anomaly cases. Using machine learning to flag anomalies in network and sensor streams, Isolation Forest achieved the strongest test performance. The system detected threats such as man-in-the-middle, denial of service, and port scanning, along with GPS spoofing and rangefinder data manipulation. Deployment on a Raspberry Pi companion computer demonstrates real-world feasibility and supports future research through the dataset and approach.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Drones through Unsupervised Machine  \nLearning  \nby  \nLei (Brian) Zhu  \nB.Eng, Honam University, Republic of Korea (South Korea), 2016  \nA Report Submitted in Partial Fulfillment  \nof the Requirements for the Degree of  \nMASTER OF ENGINEERING  \nin the Department of Electrical and Computer Engineering  \nã Lei Zhu, 2024  \nUniversity of Victoria  \nAll rights reserved. This report may not be reproduced in whole or in part, by photocopy or other  \nmeans, without the permission of the author.  \nSupervisory Committee  \nComprehensive Study on Drone Security Monitoring System: Design,  \nImplementation, and Evaluation  \nby  \nLei (Brian) Zhu  \nB.Eng, Honam University, Republic of Korea (South Korea), 2016  \nSupervisory Committee  \nDr. Issa Traoré, Supervisor  \n(Department of Electrical and Computer Engineering)  \nDr. Sherif Saad, Co-Supervisor  \n(Department of Electrical and Computer Engineering)  \nAbstract  \nThe increasing deployment of drones in various sectors has raised significant security concerns regarding their vulnerability to cyber-attacks and sensor data manipulation. Our project presentsa comprehensive anomaly detection system designed to enhance drone security and safety through unsupervised machine learning. The implementation utilized a custom drone platform based on open-source components, enabling complete access to system internals and sensor data for security monitoring.  \nThrough systematic vulnerability analysis and testing, the project collected an extensive dataset comprising approximately 11.5 million network traffic samples and 1.25 million MAVLink messages, representing both normal operations and various network-based attacks and sensor anomaly scenarios. The detection system employs machine learning algorithms to identify anomalies in both network communications and sensor data streams, with the Isolation Forest algorithm demonstrating superior performance in testing.  \nThe implemented system successfully detected various security threats, including network-based attacks such as man-in-the-middle attacks, denial of service, and port scanning, as well as sensor anomalies including GPS spoofing and rangefinder data manipulation. The deployment on a Raspberry Pi companion computer demonstrated the system's practical viability for real-world applications. This research contributes to the field of drone security by providing a systematic approach to anomaly detection and a comprehensive dataset for future research.  \nTable of Contents  \nSupervisory Committee .................................................................................................................. ii  \nAbstract.......................................................................................................................................... iii  \nTable of Contents ............................................................................................................................iv  \nList of Tables ................................................................................................................................ vii  \nList of Figures.............................................................................................................................. viii  \nGlossary ...........................................................................................................................................x  \nAcknowledgments ..........................................................................................................................xi  \nDedication..................................................................................................................................... xii  \nChapter 1: Introduction.................................................................................................................... 1  \n1.1 Context ..........................................................................................................................","cbCaipi9vkTMgDrW","https://ap.wps.com/l/cbCaipi9vkTMgDrW","pdf",27219307,66,"English","# Supervisory Committee\n# Abstract\n# Table of Contents\n# List of Tables\n# List of Figures\n# Glossary\n# Acknowledgments\n# Dedication\n# Chapter 1: Introduction\n## 1.1 Context\n## 1.2 Project Objectives\n## 1.3 Related Work\n## 1.4 Report Structure\n# Chapter 2: System Implementation\n## 2.1 System Architecture Overview\n## 2.2 Hardware Configuration\n## 2.3 Software Configuration","[{\"question\":\"What security and safety problem does the report address for quadcopter drones?\",\"answer\":\"It targets vulnerabilities to cyber-attacks and sensor data manipulation that can compromise drone security and safety, and proposes an anomaly detection system to identify such threats.\"},{\"question\":\"How was the dataset created for training and evaluation?\",\"answer\":\"The project performed systematic vulnerability analysis and testing, collecting about 11.5 million network traffic samples and 1.25 million MAVLink messages representing both normal operations and attack or sensor anomaly scenarios.\"},{\"question\":\"Which anomaly detection approach performed best in testing?\",\"answer\":\"The Isolation Forest algorithm demonstrated superior performance when detecting anomalies in both network communications and sensor data streams.\"}]","Enhancing Security and Safety in Quadcopter Drones through Unsupervised Machine Learning | PDF",166]