SoundBoost: Effective RCA and Attack Detection for UAV via Acoustic Side-Channel

Unmanned Aerial Vehicles (UAVs), or drones, are emblematic examples of cyber-physical systems where computational components and physical processes integrate to enable autonomous navigation. UAVs rely heavily on sensors such as Inertial Measurement Units (IMU) and Global Positioning System (GPS) for accurate environmental awareness and control. However, the trust placed in these sensors makes UAVs vulnerable to adversarial attacks that compromise the UAV’s operational integrity. While prior work focuses on detecting attacks against specific sensors, there remains a critical gap in performing Root Cause Analysis (RCA) to determine which component failed and why – especially under ambiguous or conflicting sensor reports. To address this gap, we propose SoundBoost, a novel RCA framework that leverages the UAV’s acoustic side-channel (i.e., sound) to diagnose navigation failures and attribute them to specific sensor compromises. While SoundBoost detects attacks by validating GPS and IMU sensor data, it focuses on post-incident diagnosis. SoundBoost conducts post-incident RCA by extracting robust acoustic signatures and using machine learning to cross-validate reported kinematics against physical behavior. We deploy SoundBoost on a UAV and evaluate it under real-world GPS spoofing attacks and synthesized IMU biasing attacks. SoundBoost achieves 100% true positive rate for IMU attacks and over 80% for GPS spoofing, outperforming the state-of-the-art by 21% – demonstrating its effectiveness as a practical forensic tool for sensor attack RCA.