A Physics-Informed Neural Network Approach Towards Cyber Attack Detection in Vehicle Platoons

Shashank Dhananjay Vyas, Shanthan Kumar Padisala, Satadru Dey

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

Abstract

Connected and Autonomous Vehicles (CAVs) are seen as a promising solution to reduce traffic congestion, improve passenger comfort and fuel economy. Although CAVs address such needs in an effective way, they are vulnerable to cyber attacks due to their extensive utilization of communication networks. In light of this problem, we present a cyber attack detection framework for a vehicle platoon based on physics-informed neural network (PINN) framework. The proposed algorithm exploits the physics based model of the platoon as well as limited available data to detect and distinguish cyber-attacks from various sources, namely, attacks affecting communication network and attacks affecting local vehicular sensors. Essentially, the PINN framework learns an uncertain parameter from the physics model and utilizes the learned parameter knowledge to infer attack scenarios. Finally, as shown through the simulation studies, the proposed algorithm is able to detect and distinguish various cyber attacks showing its potential.

Original languageEnglish (US)
Title of host publication2023 American Control Conference, ACC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4537-4542
Number of pages6
ISBN (Electronic)9798350328066
DOIs
StatePublished - 2023
Event2023 American Control Conference, ACC 2023 - San Diego, United States
Duration: May 31 2023Jun 2 2023

Publication series

NameProceedings of the American Control Conference
Volume2023-May
ISSN (Print)0743-1619

Conference

Conference2023 American Control Conference, ACC 2023
Country/TerritoryUnited States
CitySan Diego
Period5/31/236/2/23

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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