On the Feasibility of Fingerprinting Collaborative Robot Network Traffic

TL;DR

This paper investigates privacy risks in encrypted collaborative robot network traffic, demonstrating that traffic analysis techniques can accurately identify robotic actions and highlighting challenges in developing effective defenses.

Contribution

It introduces a traffic classification method using signal processing for high accuracy and evaluates the effectiveness of defenses like packet padding and timing manipulation.

Findings

High accuracy in robotic action identification using traffic analysis

Encrypted traffic remains vulnerable to privacy breaches

Defenses face challenges balancing privacy and network efficiency

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.