Modeling Cyber Threats In Autonomous Guided Vehicles Using Mean Field Models

Autonomous Guided Vehicles are one of the hottest research and industrial topics, with applications spanning from alternative transport, security and logistic. However, safety and security is usually one of the main concerns in this type of technology, limiting their application and introduction. In this paper we will consider the effects that a malicious user can cause on the system, and we propose an analysis technique based on a mean-field model, to be able to quickly obtain estimates of the consequences of an attack, and the impact of the counter-measures.

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Publication Details

Published
2025-06-24
DOI
https://doi.org/10.7148/2025-0613
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
0.00

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article

Modeling Cyber Threats In Autonomous Guided Vehicles Using Mean Field Models

Mauro Iacono, Enrico Barbierato, Serena Curzel, Alice Gatti et al.
Adversarial Robustness in Machine Learning
article

Modeling Cyber Threats In Autonomous Guided Vehicles Using Mean Field Models

Mauro Iacono, Enrico Barbierato, Serena Curzel, Alice Gatti, Marco Gribaudo
article en

Abstract

Autonomous Guided Vehicles are one of the hottest research and industrial topics, with applications spanning from alternative transport, security and logistic. However, safety and security is usually one of the main concerns in this type of technology, limiting their application and introduction. In this paper we will consider the effects that a malicious user can cause on the system, and we propose an analysis technique based on a mean-field model, to be able to quickly obtain estimates of the consequences of an attack, and the impact of the counter-measures.

European Commission, Università Cattolica del Sacro Cuore, Università degli Studi della Campania Luigi Vanvitelli, Horizon 2020 Framework Programme
Openalex Percentile: Top 91%
Adversarial Robustness in Machine Learning
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