## 2 Literature Review
### Smart Mobility
#### 2.2.2 Countermeasures
Autonomous cars do, however, run the risk of traditional cyberattacks on the information and operation of the vehicle, as well as a new breed of attacks surrounding things, such as ransomware, IoT attacks, and DDoS attacks (connected vehicles drafted into Botnet Armies). Because of their interconnectednature, security risks are associated with the networks to which they are connected. This is true regardless of the financial networks that process payments, roadside sensor networks, electricity infrastructure, or traffic control features. The authors of this paper [53] propose a method for designing safe and secure mixed traffic systems, including automated vehicles and non-automated road users, such as pedestrians, bicyclists, and conventional vehicles. This will allow the authors to model safe and secure cooperating automated vehicles and road infrastructure. In addition, this method will enable the design of safer and more secure automated vehicles and road infrastructure. The applicability of the suggested approach is shown with the help of a typical scenario involving the interaction of an automated vehicle with pedestrians at an intersection that does not have traffic signals. The development of connected vehicles, which produce dynamic data through wireless communications, has made it possible to automate vehicles to operate more effectively. This is especially true in traffic signal control, which serves as the structural foundation for the scheduling of traffic flow. On the other hand, wireless communication channels are susceptible to cyberattacks and may constitute a significant risk to dynamic traffic signal control systems. Attackers might manipulate the usual traffic flow to bring up extreme traffic congestion. The authors of this work use deep reinforcement learning to create an intelligent Sybil attack on a traffic intersection. In this attack, connected vehicles with fake identities are optimally placed to change traffic signal timings by corrupting traffic data. This work aims to highlight and exploit existing vulnerabilities in traffic signal control systems. The findings indicate that this attack causes a sizeable increase in the time it takes for vehicles to complete their journeys and results in catastrophic traffic congestion, mainly if carried out for an extended period. This will lead to several serious issues, including increased fuel consumption and air pollution in cities with a high population density. In the face of such sophisticated assaults, the design assumptions behind present traffic signal control systems have become increasingly suspect [54]. The authors of this research paper [55] establish an assessment methodology for cyber attacks on autonomous cars using preexisting traffic flow models as the basis for their work. They consider the percentage of cyber-attacked cars, the intensity and range of cyber attacks, and transportation demand. Efficiency, safety, emissions, and fuel consumption are used to evaluate the transportation system's performance. Simulations show that as the number of cyber-attacked vehicles and the severity of attacks increase, the negative impact on traffic flow grows. This reduces capacity and increases rear-end collision risk, air pollution, fuel consumption, etc. Cyberattacks on location rather than speed may cause accidents and reduce traffic efficiency. Position-attacked traffic systems consume more energy and produce more pollution. This research can be used to project future cyberattack traffic, evaluate transportation systems, and manage automated highway systems from a network security perspective. However, as is easily understood, the big problem in smart mobility is the cyber issues of mass transportation in which many people are carried within a single vehicle. Specifically, the most serious difficulty is the cyber security of public transit [56; 57]. Trains, for example, are the most often used mode of transportation in a modern city, with millions of daily passengers, as opposed to car transportation.