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 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 of the most common approaches, allowing the identification of the most used Machine Learning (ML) algorithms, architectures and datasets used.

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Abstract-Outdoor camera networks are becoming ubiquitous in critical urban areas of large cities around the world. Although current applications of camera networks are mostly limited to video surveillance, recent research projects are exploiting advances on outdoor robotics technology to develop systems that put together networks of cameras and mobile robots in people assisting tasks. Such systems require the creation of robot navigation systems in urban areas with a precise calibration of the distributed camera network. Despite camera calibration has been an extensively studied topic, the calibration (intrinsic and extrinsic) of large outdoor camera networks with no overlapping view fields, and likely to suffer frequent recalibration, poses novel challenges in the development of practical methods for user-assisted calibration that minimize intervention times and maximize precision. In this paper we propose the utilization of Laser Range Finder (LRF) data covering the area of the camera network to support the calibration process and develop a semi-automated methodology allowing quick and precise calibration of large camera networks. The proposed methods have been tested in a real urban environment and have been applied to create direct mappings (homographies) between image coordinates and world points in the ground plane (walking areas) to support person and robot detection and localization algorithms.

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