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Unmanned aerial vehicles (UAVs) have various applications in different settings, including e.g., surveillance, packet delivery, emergency response, data collection in the Internet of Things (IoT), and connectivity in cellular networks. However, this technology comes with many risks and challenges such as vulnerabilities to malicious cyber-physical attacks. This paper studies the problem of path planning for UAVs under GPS sensor permanent faults in a cyber-physical system (CPS) perspective. Based on studying and analyzing the CPS architecture of the UAV, the cyber “attacks and threats” are differentiated from attacks on sensors and communication components. An efficient way to address this problem is to introduce a novel approach for UAV’s path planning resilience to GPS permanent faults artificial potential field algorithm (RCA-APF). The proposed algorithm completes the three stages in a coordinated manner. In the first stage, the permanent faults on the GPS sensor of the UAV are detected, and the UAV starts to divert from its initial path planning. In the second stage, we estimated the location of the UAV under GPS permanent fault using Received Signal Strength (RSS) trilateration localization approach. In the final stage of the algorithm, we implemented the path planning of the UAV using an open-source UAV simulator. Experimental and simulation results demonstrate the performance of the algorithm and its effectiveness, resulting in efficient path planning for the UAV.
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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.
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- Award ID(s):
- 2229876
- PAR ID:
- 10662553
- Publisher / Repository:
- 2025 55th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript
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