SecureUAV: Energy-efficient malware detection in Unmanned Aerial Vehicles via advanced AI models

  • Project period: 2022
  • Category: Academic Development


In this project, 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 AI and human-understandable decision support models, we will build and evaluate a resilient mechanism to detect malicious activities and cyber-physical threats as well as to ensure a timely incident response by drone operator. Moreover, the goal is to propose a cybersecurity-awareness protocol and ensure energy-efficient communication. This research is aimed at bridging and strengthening the EU-US cooperation between Songlab at Embry-Riddle Aeronautical University in Florida and SmartSecLab at Kristiania University College.


The project is financed by EC/H2020


  • Andrii Shalaginov

    Andrii Shalaginov

    • Project manager
    • Førsteamanuensis

    Kristiania University College

    School EIT faglig

    Andrii Shalaginov