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Uncovering robot joint-level controller actions from encrypted network traffic: Empirical attacks and information-theoretic bounds
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DOI：10.1016/j.cose.2026.105018.png)
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Abstract
En 中文
This study examines the privacy risks associated with the teleoperation of robots controlled via encrypted network communications. From the perspective of a network eavesdropper, we explore the potential to infer sensitive robotic actions by analyzing traffic metadata, such as packet timing, size, and direction. We investigate this threat using a smartphone’s Inertial Measurement Unit (IMU) to control a collaborative robotic arm via three joint-level modalities—position, velocity, and torque—to perform four distinct actions. First, empirical traffic analysis demonstrates that an adversary can identify robot actions with high accuracy using standard machine learning classifiers. Second, to determine whether the remaining classification errors stem from empirical model limitations or the structural constraints of the physical protocols, we apply a classifier-agnostic information-theoretic evaluation. Using mutual information, we derive Bayes optimal accuracy bounds and show that torque control leaks more deterministic information than suggested by empirical models, while velocity inherently exposes less information. Third, by prototyping a traffic padding defense, we evaluate the limitations of standard obfuscation against these structural leakages. Our findings highlight the necessity of information-theoretic bounds in privacy research and inform the design of secure robotic teleoperation APIs.
Keywords:
Encrypted traffic analysis
Privacy
Robot teleoperation
Information theory
Traffic fingerprinting
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