Detecção incremental de comportamento malicioso em VANETs
Resumo
Falsificação de posição em C-ITS pode causar efeitos desastrosos no trânsito, sendo um problema em aberto na literatura. Este trabalho apresenta o DISMISS-BSM, um algoritmo capaz de identificar os ataques de falsificação de posição, utilizando dois novos atributos preditores e uma nova forma de agrupamento das mensagens em janelas deslizantes de análise de tamanho incremental, aliando agilidade e eficácia na detecção. Para avaliação do desempenho foram utilizados cinco modelos de aprendizado de máquina. Destacam-se o desempenho na detecção dos ataques random offset, superando os demais trabalhos da área, e o custo-benefício do modelo Decision Tree, com média de tempo de treinamento e inferência, de 47,617 e 0,085 seg, respectivamente.
Palavras-chave:
VANETs, comportamento malicioso, aprendizado de máquina
Referências
Committee, V. C. T. (2020). V2X Communications Message Set Dictionary.
De Carvalho Bertoli, G., Pereira Junior, L. A., Saotome, O., Dos Santos, A. L., Verri, F., A. N., Marcondes, C. A. C., Barbieri, S., Rodrigues, M. S., and Parente De Oliveira, J. M. (2021). An end-to-end framework for machine learning-based network intrusion detection system. IEEE Access, 9:106790–106805.
Ercan, S., Ayaida, M., and Messai, N. (2021). New features for position falsification detection in vanets using machine learning. In ICC 2021 - IEEE International Conference on Communications, pages 1–6.
Ercan, S., Ayaida, M., and Messai, N. (2022). Misbehavior detection for position falsification attacks in vanets using machine learning. IEEE Access, 10:1893–1904.
Gohar, A. and Nencioni, G. (2021). The role of 5g technologies in a smart city: The case for intelligent transportation system. Sustainability, 13(9).
Grover, H., Alladi, T., Chamola, V., Singh, D., and Choo, K.-K. R. (2021). Edge computing and deep learning enabled secure multitier network for internet of vehicles. IEEE Internet of Things Journal, 8(19):14787–14796.
Gyawali, S. and Qian, Y. (2019). Misbehavior detection using machine learning in vehicular communication networks. In ICC 2019 - 2019 IEEE International Conference on Communications (ICC), pages 1–6.
Hawlader, F., Boualouache, A., Faye, S., and Engel, T. (2021). Intelligent misbehavior detection system for detecting false position attacks in vehicular networks. In 2021 IEEE International Conference on Communications Workshops (ICC Workshops).
IEEE (2016). Ieee standard for wireless access in vehicular environments–security services for applications and management messages. IEEE Std 1609.2-2016 (Revision of IEEE Std 1609.2-2013), pages 1–240.
Kamoi, R. N., Junior, L. A. P., Verri, F. A. N., Marcondes, C. A. C., Ferreira, C. H. G., Meneguette, R. I., and Cunha, A. M. D. (2021). Platoon grouping network offloading mechanism for vanets. IEEE Access, 9:53936–53951.
Sharma, A. and Jaekel, A. (2022). Machine learning based misbehaviour detection in vanet using consecutive bsm approach. IEEE Open Journal of Vehicular Technology.
So, S., Petit, J., and Starobinski, D. (2019). Physical layer plausibility checks for misbehavior detection in v2x networks. In Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks, WiSec ’19, page 84–93, New York, NY, USA. Association for Computing Machinery.
So, S., Sharma, P., and Petit, J. (2018). Integrating plausibility checks and machine learning for misbehavior detection in vanet. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 564–571.
Uprety, A., Rawat, D. B., and Li, J. (2021). Privacy preserving misbehavior detection in iov using federated machine learning. In 2021 IEEE 18th Annual Consumer Communications Networking Conference (CCNC), pages 1–6.
van der Heijden, R. W., Dietzel, S., Leinmuller, T., and Kargl, F. (2019). Survey on misbehavior detection in cooperative intelligent transportation systems. IEEE Communications Surveys Tutorials, 21(1):779–811.
van der Heijden, R. W., Lukaseder, T., and Kargl, F. (2018). Veremi: A dataset for comparable evaluation of misbehavior detection in vanets. In Beyah, R., Chang, B., Li, Y., and Zhu, S., editors, Security and Privacy in Communication Networks, pages 318–337, Cham. Springer International Publishing.
De Carvalho Bertoli, G., Pereira Junior, L. A., Saotome, O., Dos Santos, A. L., Verri, F., A. N., Marcondes, C. A. C., Barbieri, S., Rodrigues, M. S., and Parente De Oliveira, J. M. (2021). An end-to-end framework for machine learning-based network intrusion detection system. IEEE Access, 9:106790–106805.
Ercan, S., Ayaida, M., and Messai, N. (2021). New features for position falsification detection in vanets using machine learning. In ICC 2021 - IEEE International Conference on Communications, pages 1–6.
Ercan, S., Ayaida, M., and Messai, N. (2022). Misbehavior detection for position falsification attacks in vanets using machine learning. IEEE Access, 10:1893–1904.
Gohar, A. and Nencioni, G. (2021). The role of 5g technologies in a smart city: The case for intelligent transportation system. Sustainability, 13(9).
Grover, H., Alladi, T., Chamola, V., Singh, D., and Choo, K.-K. R. (2021). Edge computing and deep learning enabled secure multitier network for internet of vehicles. IEEE Internet of Things Journal, 8(19):14787–14796.
Gyawali, S. and Qian, Y. (2019). Misbehavior detection using machine learning in vehicular communication networks. In ICC 2019 - 2019 IEEE International Conference on Communications (ICC), pages 1–6.
Hawlader, F., Boualouache, A., Faye, S., and Engel, T. (2021). Intelligent misbehavior detection system for detecting false position attacks in vehicular networks. In 2021 IEEE International Conference on Communications Workshops (ICC Workshops).
IEEE (2016). Ieee standard for wireless access in vehicular environments–security services for applications and management messages. IEEE Std 1609.2-2016 (Revision of IEEE Std 1609.2-2013), pages 1–240.
Kamoi, R. N., Junior, L. A. P., Verri, F. A. N., Marcondes, C. A. C., Ferreira, C. H. G., Meneguette, R. I., and Cunha, A. M. D. (2021). Platoon grouping network offloading mechanism for vanets. IEEE Access, 9:53936–53951.
Sharma, A. and Jaekel, A. (2022). Machine learning based misbehaviour detection in vanet using consecutive bsm approach. IEEE Open Journal of Vehicular Technology.
So, S., Petit, J., and Starobinski, D. (2019). Physical layer plausibility checks for misbehavior detection in v2x networks. In Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks, WiSec ’19, page 84–93, New York, NY, USA. Association for Computing Machinery.
So, S., Sharma, P., and Petit, J. (2018). Integrating plausibility checks and machine learning for misbehavior detection in vanet. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 564–571.
Uprety, A., Rawat, D. B., and Li, J. (2021). Privacy preserving misbehavior detection in iov using federated machine learning. In 2021 IEEE 18th Annual Consumer Communications Networking Conference (CCNC), pages 1–6.
van der Heijden, R. W., Dietzel, S., Leinmuller, T., and Kargl, F. (2019). Survey on misbehavior detection in cooperative intelligent transportation systems. IEEE Communications Surveys Tutorials, 21(1):779–811.
van der Heijden, R. W., Lukaseder, T., and Kargl, F. (2018). Veremi: A dataset for comparable evaluation of misbehavior detection in vanets. In Beyah, R., Chang, B., Li, Y., and Zhu, S., editors, Security and Privacy in Communication Networks, pages 318–337, Cham. Springer International Publishing.
Publicado
12/09/2022
Como Citar
DUTRA, Fernando; BONFIM, Kenniston; TRAVAGINI, Carlos; MENEGUETTE, Rodolfo I.; SANTOS, Aldri; PEREIRA, Lourenço Alves.
Detecção incremental de comportamento malicioso em VANETs. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 22. , 2022, Santa Maria.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 125-138.
DOI: https://doi.org/10.5753/sbseg.2022.225164.
