# Resistance to Cybersecurity Attacks in a Novel Network for Autonomous Vehicles
## 1 Introduction  
Vehicular edge computing is the integration of emerging mobile edge computing with traditional vehicular networks, and it aims to move communication, computing, and caching resources to close proximity of vehicular users to support V2V, V2I and V2X communications. Autonomous Vehicular edges use a wide range of sensors spanning traditional (such as position, acceleration, temperature) and also state-of-the-arts camera LIDER radar to improve their reliability and provide a range of new services and applications for users (such as virtual reality and smart control). In order to explore and identify the disruption caused to messages by attacking nodes in an opportunistic network, multiple experiments are run. A smart city is used to run the experiments in a controlled environment, changing the style, quantity, and length of attack to provide a multi-dimensional analysis into the protocol that provides best resistance to the attacks. In the city the public transport systems (trams and buses) are autonomous and rely on the information sent through the opportunistic network to function. Fast emerging new sensor devices and 5G+ sensor networks are becoming increasingly pervasive, and we see an explosion of new autonomous vehicle edge applications in which vehicles can sense, store process, communicate, predictand anticipate any information about themselves, other vehicles, and the environments. The messages include information about traffic, roadside infrastructure, and the location of vehicles around the trams and buses. Without information, the autonomous vehicles would not know where the surrounding cars and pedestrians are so might collide with them. Even if collisions were not caused, traffic hold-ups are a legitimate cause for concern when high risk targets are vulnerable. In order to test the networking protocols, modified algorithms were produced that allowed cars either to be fully functional, or malfunction as black and grey hole nodes. When the attack starts, cars will either act as black or grey hole nodes depending on which the attackers select. The attackers aim to disrupt the flow of the network and cause harm to those within the city. As the trams and buses are using the data to navigate and manoeuvre around the city, if the data do not reach them they will either not reach their destination or act in a dangerous manner as they do not have the data to avoid doing so. In order to study the nodes throughout the attacks, each of them collects data about each of the messages they receive. The data consist of how long the messages have been stored at the current node, the time at which they were originally sent and the time they were delivered. With the large amount of data gathered, multi-dimensional exploration allowed an insight into how different attacks impact metrics including the sensors' delivery probability of a message, the sensors' time taken to deliver and the computation expense to each node. The analysis of the attacks shows how different algorithms are affected by black and grey hole attacks. The state-of-the-art Maxprop algorithm is compared to a benchmark protocol (Epidemic) as well as other more complex methods. Throughout the paper, novel networks are referred to. These are new, emerging types of networks which combine autonomous vehicles (as mobile cyber physical systems) with rich onboard sensors, cameras that are able to share and process complex real time multimedia data to help their decisions. Figure 1[1] shows a method of how vehicles may communicate in a novel network using a hop-based approach. This method allows all vehicles to send/receive messages that are insecure. Security is important when working in safety-critical systems (such as with heavy machinery) and, as such, requires further investigation. The experiments in this paper consider the security provided by different delay-tolerant networking protocols and how they could improve the performance of the network.