Context-Aware Operational Security for Autonomous Drones

RESUMO

Autonomous drone-based services have been gaining significant interest across various application domains due to their mobility, flexibility, cost-effectiveness, and capability to integrate various sensors and actuators. However, operational failures or cyberattacks targeting drone systems can lead to severe economic impacts and safety concerns. Hence, ensuring secure and reliable autonomous drone operations is essential for the safe deployment of drone-enabled services. Nevertheless, the traditional security measures fall short due to drones' limited computational resources and power budget (battery), as well as the temporal and sequential behavior of drone operations due to drones' mobile nature. In this paper, we address this gap by utilizing Recurrent Neural Networks (RNNs), specifically focusing on Long Short-Term Memory (LSTM) networks, for autonomous drone operation security and reliability due to their temporal and sequential analysis capabilities. We leverage these capabilities for anomaly detection in autonomous drone sensor data and operation commands, which we refer to as Denial of Usage Detection Engine IDS (DUDE-IDS). We integrated the proposed DUDE-IDS into the drone mission computer (i.e., operating directly on drones rather than an edge node or ground control stations) to monitor data flows and to detect potential threats in real-time. Extensive experimental results demonstrate the effectiveness of this approach in identifying anomalies associated with GPS spoofing, Man-in-the-Middle (MITM), replay, and Denial-of-Service (DoS) attacks with 98% accuracy. We also evaluate our resource utilization and power consumption under different configurations, demonstrating the applicability of our approach in active drone operations in real-time.