Machine Learning Security of Connected Autonomous Vehicles: A Systems Perspective

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Machine Learning security is vital for the safe operation
of Autonomous Vehicles. When Autonomous Vehicles are
connected and cooperating, they form a system of systems that
have shared objectives. However, adversarial environments and
adversarial vehicles in the system can cause security challenges
for the whole system. Current research focuses on the Machine
Learning security challenges from the perspective of a single
vehicle. We argue that there is a need to consider these security
challenges from the perspective of multiple interconnected vehicles,
as a system. In this paper, we explore these challenges from
the perspective of many Connected Autonomous Vehicles as a
system with respect to Machine Learning security. We include
attack scenarios that demonstrate the system interactions that
can lead to cascading failures, which test the resilience of the
system. We also outline some of the challenges in researching this
perspective, where a key challenge is identifying indicators and
metrics to describe the system resilience when under attack. To
observe the system, experimentation via simulation is identified
as a suitable environment that can capture the complex and
dynamic system interactions in this security context.
Original languageEnglish
Title of host publication2024 IEEE International Conference on Industrial Technology (ICIT)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication statusAccepted/In press - 2024
EventThe IEEE International Conference on Industrial Technology - Bristol, United Kingdom
Duration: 25 Mar 202427 Mar 2024
Conference number: 25
https://icit2024.ieee-ies.org/

Conference

ConferenceThe IEEE International Conference on Industrial Technology
Abbreviated titleICIT
Country/TerritoryUnited Kingdom
Period25/03/2427/03/24
Internet address

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