Guaranteeing Spoof-Resilient Multi-Robot Networks

Abstract

Multi-robot networks use wireless communication to provide wide-ranging services such as aerial surveillance and unmanned delivery. However, effective coordination between multiple robots requires trust, making them particularly vulnerable to cyber-attacks. Specifically, such networks can be gravely disrupted by the Sybil attack, where even a single malicious robot can spoof a large number of fake clients. This paper proposes a new solution to defend against the Sybil attack, without requiring expensive cryptographic key-distribution. Our core contribution is a novel algorithm implemented on commercial Wi-Fi radios that can sense spoofers using the physics of wireless signals. We derive theoretical guarantees on how this algorithm bounds the impact of the Sybil Attack on a broad class of multirobot problems, including locational coverage and unmanned delivery. We experimentally validate our claims using a team of AscTec quad rotor servers and iRobot Create ground clients, and demonstrate spoofer detection rates over 96%.

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Document Details

Document Type
Technical Report
Publication Date
Feb 12, 2016
Accession Number
AD1033848

Entities

People

  • Daniela L. Rus
  • Dina Katabi
  • Mark Mazumder
  • Stephanie Gil
  • Swarun Kumar

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Acoustic Absorption
  • Algorithms
  • Anechoic Chambers
  • Communication Channels
  • Computer Communications
  • Control Systems
  • Detection
  • Detectors
  • Robotic Swarms
  • Robots
  • Sensor Networks
  • Signal Processing
  • Synthetic Aperture Radar
  • Three Dimensional
  • Wireless Communications
  • Wireless Computer Networks
  • Wireless Networks

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Cybersecurity.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Cyber
  • Cyber - Quantum