Incremental detection of misbehavior in VANETs

• Fernando Dutra ITA / Brazilian Army http://orcid.org/0000-0001-5367-8622
• Kenniston Bonfim ITA https://orcid.org/0000-0003-0666-5663
• Carlos Travagini ITA https://orcid.org/0000-0002-1372-2569
• Rodolfo I. Meneguette USP https://orcid.org/0000-0003-2982-4006
• Aldri Santos UFMG https://orcid.org/0000-0002-5861-4414
• Lourenço Alves Pereira ITA https://orcid.org/0000-0002-9682-0075

DOI: https://doi.org/10.5753/sbseg.2022.225164

Abstract

Position forgery in VANETs may cause disastrous effects on traffic. This work presents the DISMISS-BSM, an algorithm that is able to detect this kind of misbehavior in C-ITS enviroment, resorting to two new features and a new way of grouping the messages in sliding analysis windows of incremental size, combining agility and efficiency in the misbehavior detection. To evaluate the performance, five machine learning models were used, showing remarkable results in detecting random offset type attacks, surpassing other works in the area, and the cost-benefit of the Decision Tree model, with an average training and inference time of, 47.617 and 0.085 sec, respectively.

Keywords: VANETs, misbehavior, machine learning

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