# The Impact of Spoofing Attacks in Connected Autonomous Vehicles under Traffic Congestion Conditions
## 2 Related Work  
Modern vehicles are equipped with cutting-edge technology such as cameras, sensors, and other smart devices to monitor, transmit, and receive information. These store personal data such as messages, financial information, etc. Various malicious actors try to gain remote access to the stored information and steal it. There are various forms of autonomous vehicle attacks, but there are also various means of protection against them.  
In [9], the authors propose a model called self-stabilizing cyber-attack for connected vehicles. After providing a theoretical model, the authors validate their approach through simulation results. In [10], the authors present a literature review on CAV technology. CAVs and emotional intelligence are also investigated. Furthermore, in [11], the authors collect data from traffic simulations and investigate the effects on traffic caused by cyber-attacks. The analysis is done considering time-to-collision and deceleration rate to avoid collision, which represent safety factors. The simulation results show the impact of time-delay attacks, which also use location information, in road throughput. It was concluded that heavy traffic greatly affects the implications of cyber-attacks. A literature review paper is presented in [7], where the authors investigate CAV security tasks. The authors conclude that the use of cloud and artificial intelligence (AI) techniques represent an efficient way to prevent CAV cyber-attacks.  
In [12], the authors mention that distributed denial of service (DDOS) represents an important category of attacks on VANETs: they provide a survey and a layer-by-layer analysis of this type of attacks. In [8], the authors examine types of cyber-attacks, like message falsification, dedicated denial of service, and spoofing. One of the research findings is the non-stability of the traffic stream and CAV string in all three types of cyber-attacks. Another research finding (for a single CAV) is the creation of high traffic stream disruption at the scenario of a slight cyber-attack.  
Reference [13] gives a review of cyber-attack simulation models. Furthermore, the authors suggest a domain-specific attack model for self-driving vehicles. Finally, having in mind the decision-tree method, a classification of cyber-attacks is shown. Additionally, for the case of short-term memory networks, the authors predict the type of attack. A CAV cyber-attack survey work is presented in [14]. Moreover, in this work, the authors present different mitigation strategies from the vehicle manufacturers' and governments' point of view.  
However, all these literature review papers do not consider a cross-layer approach in their designs. Furthermore, statistical parameters like CoV are not considered. Therefore, having in mind the research gaps mentioned above, in this study, we develop a microscopic traffic simulation platform in order to analyse the traffic impacts of cyber-attacks on CAVs. Many times the theoretical approach is far from reality even under normal conditions. Therefore, initially the goal is to see what the effects are and what OSI layer of simple DoS attacks are affected in order to design an overall traffic simulation model that will incorporate a cross-layer probabilistic detection system.