Design and modeling an Adaptive NeuroDiffuse System (ANFIS) for the prediction of a security index in VANET Vehicular Ad hoc NETworks (VANET) are networks that allows communication between vehicles using their own connection infrastructure. There are several advantages and applications in using this technology; one of most significant is road safety. As in most other networks, it is very important to guarantee the transport and the security of information. The security in VANET is a big challenge because there are different types of attacks that endanger communications. This paper proposes an adaptive neurofuzzy inference system (ANFIS) applied to obtain a prediction model of security index in VANET. The research process starts with the network simulation to obtain the database that is prepared and analyzed statistically. Finally, using MATLAB toolbox, we show the proposed model of security level that allows estimating the network vulnerability in the event of an attack.

A Systematic Review on Intelligent Intrusion Detection Systems for VANETs Vehicular Ad hoc Networks (VANETs) are a growing area that continues to gain interest with an increasing diversity of applications available. These are the underlying network for Intelligent Transportation Systems (ITS), a set of applications and services that aim to provide greater security and comfort to drivers and passengers. However, the characteristics and size of a VANET make it a security challenge. It has been a subject of study, with several research works aimed at this problem, usually involving cryptography. There are, however, some attacks that cannot be solved using traditional methodologies. For example, Sybil attack, Denial of Service (DoS), Black Hole, etc. are not preventable using cryptographic tools. Nonetheless, using an Intrusion Detection System (IDS) can help to detect malicious behavior, preventing further damage. This work presents a Systematic Literature Review (SLR) that aims to evaluate the feasibility of this type of solution. Additionally, it should provide information about the most common approaches, allowing the identification of the most used Machine Learning (ML) algorithms, architectures and datasets.