Physical Invariant Based Attack Detection for Autonomous Vehicles: Survey, Vision, and Challenges

Francis Akowuah, Fanxin Kong

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

4 Scopus citations

Abstract

Automobiles continue to become more autonomous and connected as increasingly integrating with information technology. Meanwhile, this advance also comes with a higher risk of various security violations on vehicles. In this paper, we study how to detect attacks on autonomous vehicles, and specially focus on physical invariant-based attack detection. A physical invariant (PI) is defined as a property that a physical system always holds, i.e., the evolution of system states (usually measured by sensors) follows immutable physical laws. We first discuss existing research efforts of PI-based attack detection and classify them according to the knowledge of physical invariants and sensor redundancy. Then, we point out several critical challenges on attack detection research efforts including data sets, benchmark and testbeds, and evaluation metrics. Finally, we highlight open problems that offer promising research opportunities.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 4th International Conference on Connected and Autonomous Driving, MetroCAD 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages31-40
Number of pages10
ISBN (Electronic)9781665445948
DOIs
StatePublished - Apr 2021
Event4th International Conference on Connected and Autonomous Driving, MetroCAD 2021 - Detroit, United States
Duration: Apr 28 2021Apr 29 2021

Publication series

NameProceedings - 2021 4th International Conference on Connected and Autonomous Driving, MetroCAD 2021

Conference

Conference4th International Conference on Connected and Autonomous Driving, MetroCAD 2021
Country/TerritoryUnited States
CityDetroit
Period4/28/214/29/21

Keywords

  • attack detection
  • autonomous driving
  • autonomous vehicles
  • physical invariant
  • self-driving

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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