Poster: Automated Discovery of Sensor Spoofing Attacks on Robotic Vehicles

Kyeongseok Yang, Sudharssan Mohan, Yonghwi Kwon, Heejo Lee, Chung Hwan Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Robotic vehicles are playing an increasingly important role in our daily life. Unfortunately, attackers have demonstrated various sensor spoofing attacks that interfere with robotic vehicle operations, imposing serious threats. Thus, it is crucial to discover such attacks earlier than attackers so that developers can secure the vehicles. In this paper, we propose a new sensor fuzzing framework SensorFuzz that can systematically discover potential sensor spoofing attacks on robotic vehicles. It generates malicious sensor inputs by formally modeling the existing sensor attacks and leveraging high-fidelity vehicle simulation, and then analyzes the impact of the inputs on the vehicle with a resilience-based feedback mechanism.

Original languageEnglish
Title of host publicationCCS 2022 - Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Pages3503-3505
Number of pages3
ISBN (Electronic)9781450394505
DOIs
Publication statusPublished - 2022 Nov 7
Event28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022 - Los Angeles, United States
Duration: 2022 Nov 72022 Nov 11

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Conference

Conference28th ACM SIGSAC Conference on Computer and Communications Security, CCS 2022
Country/TerritoryUnited States
CityLos Angeles
Period22/11/722/11/11

Keywords

  • fuzzing
  • robotic vehicle
  • sensor spoofing

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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