Creación de un dataset para Redes Ad-Hoc Vehiculares (VANETs) a través de ataques cibernéticos simulados.

Abstract

Ad-hoc Vehicular Networks (VANETs) are intelligent vehicle networks, an important technology that offers a wide range of services, which may be related to safety, optimization and entertainment. These communications can be used to divert traffic from congested routes, for emergency aids and to prevent accidents, in addition to provide intelligent traffic control, among others. However, the security of VANETs becomes a critical challenge, since an attacker can use the same system to spread false messages that cause traffic congestion and road accidents. This document investigates the application of the detection of misconduct in simulated scenarios where an attacker interferes with the communication in the VANETs, it is based on a project of the state of art where the procedures and coding of the false position attack were studied. To do this, start the creation of an open source where Sybil and Timing attacks are addressed, by simulating these attacks you get a set of data (dataset) that will be used for data analysis, then the dataset is validated. each attack by means of the plausibility detection using detection mechanisms that with the calculation of precision and recovery can determine which is the most viable for the detection of the simulated attacks. And with that formulate a detailed documentation of the dataset for better interpretation in future studies and research.