RobIn: Robust-Invariant-Based Physical Attack Detector for Autonomous Aerial Vehicles

Autonomous aerial vehicles (UAVs) are widespread in Internet of Things (IoT) systems applications. However, UAVs suffer cyberspace security threats, especially physical attacks that leverage physics signals to deceive sensors, disrupt missions, and potentially crash the UAVs. Such attack detection demands not only guaranteeing detection accuracy and timeliness but also robustness. Prior studies consider physical laws (Invariant) to detect inconsistency, but they sacrifice robustness requirements of uncertainties and lack theoretical guarantees of detection performance. To achieve the sensitivity-specificity tradeoff, this article proposes <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RobIn</monospace>, a Robust Invariant-based physical attacks detector design incorporating scenario optimization. The key idea behind <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RobIn</monospace> is robustifying the invariant model via scenario optimization theory to ensure modality untouched and trustworthy detection. With an offline robustification scheme and an onboard detection algorithm, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RobIn</monospace> can balance attack sensitivity and robustness to uncertainties. We theoretically provide the detection specificity lower bound guarantees under highlighting sensitivity in finite scenarios. We evaluate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RobIn</monospace> in both four virtual and three real UAVs, achieving 96.2% detection rates and 1.6% false alarm rates against 6 types of existing attacks, with only 3.82% runtime overhead (on average). Moreover, we illustrate the resilience of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RobIn</monospace> against the worst-case attack.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-01-16
DOI
https://doi.org/10.1109/jiot.2025.3530095
Citations
1
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
2.48

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article

RobIn: Robust-Invariant-Based Physical Attack Detector for Autonomous Aerial Vehicles

Wenyuan Xu, Kaikai Pan, Aoran Cui, Shiang Guo et al.
1 citations
IEEE Internet of Things Journal
Anomaly Detection Techniques and Applications
2.48
article

RobIn: Robust-Invariant-Based Physical Attack Detector for Autonomous Aerial Vehicles

Wenyuan Xu, Kaikai Pan, Aoran Cui, Shiang Guo, Qidi Zhong
article en
1 citations

Abstract

Autonomous aerial vehicles (UAVs) are widespread in Internet of Things (IoT) systems applications. However, UAVs suffer cyberspace security threats, especially physical attacks that leverage physics signals to deceive sensors, disrupt missions, and potentially crash the UAVs. Such attack detection demands not only guaranteeing detection accuracy and timeliness but also robustness. Prior studies consider physical laws (Invariant) to detect inconsistency, but they sacrifice robustness requirements of uncertainties and lack theoretical guarantees of detection performance. To achieve the sensitivity-specificity tradeoff, this article proposes <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RobIn</monospace>, a Robust Invariant-based physical attacks detector design incorporating scenario optimization. The key idea behind <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RobIn</monospace> is robustifying the invariant model via scenario optimization theory to ensure modality untouched and trustworthy detection. With an offline robustification scheme and an onboard detection algorithm, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RobIn</monospace> can balance attack sensitivity and robustness to uncertainties. We theoretically provide the detection specificity lower bound guarantees under highlighting sensitivity in finite scenarios. We evaluate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RobIn</monospace> in both four virtual and three real UAVs, achieving 96.2% detection rates and 1.6% false alarm rates against 6 types of existing attacks, with only 3.82% runtime overhead (on average). Moreover, we illustrate the resilience of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RobIn</monospace> against the worst-case attack.

IEEE Internet of Things JournalVol. 12(8)
Zhejiang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 12%
Anomaly Detection Techniques and Applications
2.48
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