Enhancing security and safety in quadcopter drones through unsupervised machine learning
| dc.contributor.author | Zhu, Lei | |
| dc.contributor.supervisor | Traoré, Issa | |
| dc.contributor.supervisor | Saad, Sherif | |
| dc.date.accessioned | 2025-01-24T23:53:08Z | |
| dc.date.available | 2025-01-24T23:53:08Z | |
| dc.date.issued | 2024 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Master of Engineering MEng | |
| dc.description.abstract | The increasing deployment of drones in various sectors has raised significant security concerns regarding their vulnerability to cyber-attacks and sensor data manipulation. Our project presents a 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. Through 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. The 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. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.uri | https://hdl.handle.net/1828/21025 | |
| dc.language.iso | en | |
| dc.rights | Available to the World Wide Web | |
| dc.subject | unmanned aerial vehicles | |
| dc.subject | machine learning | |
| dc.subject | anomaly detection | |
| dc.subject | cyber security | |
| dc.subject | sensor data analysis | |
| dc.subject | network security | |
| dc.title | Enhancing security and safety in quadcopter drones through unsupervised machine learning | |
| dc.type | project |