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On the Feasibility of Fingerprinting Collaborative Robot Network Traffic

Cheng Tang, Diogo Barradas, Urs Hengartner, Yue Hu

TL;DR

This paper investigates privacy risks arising from traffic analysis of encrypted communications in collaborative robotics, focusing on script-based control interfaces. It evaluates existing website fingerprinting attacks on robot traffic and then develops a signal-processing based pipeline using convolution and correlation to extract command-pattern features, ultimately employing XGBoost for action classification. On a Kinova Gen3 dataset with four actions, the approach achieves about 97% accuracy, illustrating a strong privacy breach under encryption. Defenses including packet padding and latency-aware traffic modulation are explored, but they incur substantial bandwidth overhead or unacceptable latency, signaling a need for more practical robot-specific protections. Overall, the work highlights a significant vulnerability in encrypted robotic communications and establishes a baseline for evaluating privacy defenses in robotic operation traffic.

Abstract

This study examines privacy risks in collaborative robotics, focusing on the potential for traffic analysis in encrypted robot communications. While previous research has explored low-level command recovery in teleoperation setups, our work investigates high-level motion recovery from script-based control interfaces. We evaluate the efficacy of prominent website fingerprinting techniques (e.g., Tik-Tok, RF) and their limitations in accurately identifying robotic actions due to their inability to capture detailed temporal relationships. To address this, we introduce a traffic classification approach using signal processing techniques, demonstrating high accuracy in action identification and highlighting the vulnerability of encrypted communications to privacy breaches. Additionally, we explore defenses such as packet padding and timing manipulation, revealing the challenges in balancing traffic analysis resistance with network efficiency. Our findings emphasize the need for continued development of practical defenses in robotic privacy and security.

On the Feasibility of Fingerprinting Collaborative Robot Network Traffic

TL;DR

This paper investigates privacy risks arising from traffic analysis of encrypted communications in collaborative robotics, focusing on script-based control interfaces. It evaluates existing website fingerprinting attacks on robot traffic and then develops a signal-processing based pipeline using convolution and correlation to extract command-pattern features, ultimately employing XGBoost for action classification. On a Kinova Gen3 dataset with four actions, the approach achieves about 97% accuracy, illustrating a strong privacy breach under encryption. Defenses including packet padding and latency-aware traffic modulation are explored, but they incur substantial bandwidth overhead or unacceptable latency, signaling a need for more practical robot-specific protections. Overall, the work highlights a significant vulnerability in encrypted robotic communications and establishes a baseline for evaluating privacy defenses in robotic operation traffic.

Abstract

This study examines privacy risks in collaborative robotics, focusing on the potential for traffic analysis in encrypted robot communications. While previous research has explored low-level command recovery in teleoperation setups, our work investigates high-level motion recovery from script-based control interfaces. We evaluate the efficacy of prominent website fingerprinting techniques (e.g., Tik-Tok, RF) and their limitations in accurately identifying robotic actions due to their inability to capture detailed temporal relationships. To address this, we introduce a traffic classification approach using signal processing techniques, demonstrating high accuracy in action identification and highlighting the vulnerability of encrypted communications to privacy breaches. Additionally, we explore defenses such as packet padding and timing manipulation, revealing the challenges in balancing traffic analysis resistance with network efficiency. Our findings emphasize the need for continued development of practical defenses in robotic privacy and security.
Paper Structure (24 sections, 4 equations, 11 figures, 1 table)

This paper contains 24 sections, 4 equations, 11 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Control Interfaces in Robotics
  4. The Kinova Robotic Arm & APIs
  5. Encrypted Traffic Fingerprinting
  6. Methodology
  7. Threat Model
  8. Experimental Setup
  9. Metrics
  10. Characterization of Robot Actions
  11. Exploiting Known Attacks for Action Identification
  12. ML-based Website Fingerprinting Attacks
  13. Attacking Robot Operation Traffic
  14. Signal Processing-based Robot Action Identification
  15. Pattern Matching Operations
  16. ...and 9 more sections

Figures (11)

  • Figure 1: Depiction of our threat model.
  • Figure 2: The Kinova robotic arm executing four actions.
  • Figure 3: Network traces for two kinds of commands.
  • Figure 4: Examples of robot actions' packet lengths over time.
  • Figure 5: Examples of two "turn on switch" network traffic traces.
  • ...and 6 more figures