| Texto completo | |
| Autor(es): |
Dutra, Fernando
;
Bonfim, Kenniston
;
Siqueira, Carlos
;
Pereira, Lourenco A., Jr.
;
Santos, Aldri
;
Meneguette, Rodolfo I.
;
IEEE
Número total de Autores: 7
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022); v. N/A, p. 6-pg., 2022-01-01. |
| Resumo | |
Basic Safety Messages (BSMs) are crucial for Cooperative Intelligent Transport Systems (C-ITS) to enable signalization of events and therefore allow vehicle synchronization to avoid accidents and improve traffic flows. However, in this context, entities can maliciously alter the content of BSMs and consequently incur disastrous events to disturb the C-ITS, causing user prejudice. This paper presents DISMISS-BSM, a novel misbehavior detection for detecting BSM forgery, consisting of buffering the host's received messages and using them as predictors. We compared our solution with state-of-the-art approaches, and the results indicate that our features promote better performance in identifying message forgery. We derive predictors considering the received signal strength and a movement pattern disruption indicator through our feature engineering process. Moreover, we use different sliding window lengths (2, 3, 8, 13, 18, and 23) to predict the dynamic attacker behavior better. Our results outperform the state-of-the-art and indicate that decision trees were the better conformant among KNN, DT, MLP, and LSTM, performing the training phase in about 47 seconds on average for the signal strength and displacement compliance predictors. (AU) | |
| Processo FAPESP: | 21/06872-6 - Controle de reserva de espaço aéreo 4D para transporte inteligente |
| Beneficiário: | Cesar Augusto Cavalheiro Marcondes |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |