Energy-efficient malware detection for UAVs

Website

https://ngiatlantic.eu/funded-experiments/secureuav

For SecureUAV, the goal is to develop a platform and framework for increased cybersecurity protection and end-user awareness of cyberthreats in unmanned aerial vehicles (UAV), through the use of AI and human-understandable decision support models.

ProjectID

NGI-ATLANTIC:OC05-385

Acronym

EEMD4UAV

Additional Info

SecureUAV project’s vision is to enhance the overall operational UAVs security as well as Linux-based and similar mainboards that run mission-critical functionality, process video streams and maintain communication with drone operators through sophisticated remote controllers. Such communication is usually maintained with the help of AES-256 or similar standards, while transmitting altitude, distance, GPS location, velocities, battery level and temperature. However, there is no low-threshold information about the drone system cybersecurity status or if there are any virus infection or attack happening. So, modern UAV system needs a toolkit to provide an insight into cyber-physical cybersecurity awareness and telemetry. Even though there are available commercial cybersecurity solutions to guard Linux, such as anti-virus (AV) or intrusion detection systems (IDS), energy consumption aspects make them nearly inapplicable.

Enduser Relevance

This experiment will improve the security of Unmanned Aerial Vehicles (UAV)s by detecting malicious activities and cyber-physical threats to ensure a timely incident response by drone operator.