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


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
Number of pages3
ISBN (Electronic)9781450394505
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


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

Bibliographical note

Funding Information:
ACKNOWLEDGMENT We thank the anonymous reviewers for their insightful feedback. This work was supported in part by the University of Texas at Dallas Office of Research through the NFRS program, NSF under award number 190821, Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2019-0-01697 Development of Automated Vulnerability Discovery Technologies for Blockchain Platform Security, No.2022-0-00277 Development of SBOM Technologies for Securing Software Supply Chains, No.2022-0-01198 Convergence Security Core Talent Training Business, and No.IITP-2022-2020-0-01819 ICT Creative Consilience programs), and a gift from Cisco Systems.

Publisher Copyright:
© 2022 Owner/Author.


  • fuzzing
  • robotic vehicle
  • sensor spoofing

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications


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