Federated Learning-based Architecture for Detecting Position Spoofing in Basic Safety Messages

Autor(es)

Bonfim, Kenniston Arraes ; Dutra, Fernando da Silva ; Travagini Siqueira, Carlos Eduardo ; Meneguette, Rodolfo Ipolito ; dos Santos, Aldri Luiz ; Pereira Junior, Lourenco Alves

• Tipo de documento: Artigo Científico
• Fonte: 2023 IEEE 97TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-SPRING; v. N/A, p. 5-pg., 2023-01-01.

Resumo

Nowadays, the growth of privacy concerns imposes new requirements on security mechanisms deployed on autonomous vehicles. Assessing users' misbehavior in Cooperative Intelligent Transport Systems (C-ITS) is crucial to keep them safe. Notwithstanding, data is a valuable asset, and exchanging them (e.g., Basic Safety Message-BSM) over the external network exposes sensitive data and compromises the privacy of CITS participants. This paper presents a federated learning-based architecture that shares model parameters for position spoofing detection in C-ITS. Our solution consists of buffering the host's received messages and using them as predictors for misbehavior. To this end, we derived five novel features and group messages into analysis windows varying from 2 to 23 BSMs to predict dynamic attacker behavior better. Our results demonstrate the feasibility of training the models on the onboard unit (OBU) and sharing the models' parameters in a federated learning-based architecture. Therefore, we bring users' privacy to the table by preserving a local dataset, balancing the tradeoff between a rapid training process and reliable misbehavior detection. Moreover, our multilayer perceptron model outperforms the detecting position spoofing attacks in state-of-the-art works. (AU)

• Processo FAPESP: 20/09850-0 - Centro de Pesquisa Aplicada em Inteligência Artificial: impulsionando a transformação das indústrias rumo ao padrão 5.0
• Beneficiário: Jefferson de Oliveira Gomes
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada
• Processo FAPESP: 20/07162-0 - Serviços para um sistema de transporte inteligente
• Beneficiário: Rodolfo Ipolito Meneguette
• Modalidade de apoio: Auxílio à Pesquisa - Regular