A Systematic Review on Intelligent Intrusion Detection Systems for VANETs
Abstract
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.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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