## 9 Discussion
### Cyber Security Risks and Safety Concerns of EIAVs
#### 9.4.2 Fundamental Security Principles  
Fundamental security principles are crucial for protecting the security of autonomous vehicles and the dangers they seek to counteract. Autonomous vehicles rely on complex software and hardware systems to operate [179], and any vulnerabilities in these systems can be exploited by attackers to gain control of the vehicle or cause it to malfunction. This can result in serious safety risks for passengers, other drivers, and pedestrians. Some of the key security principles that are important for protecting autonomous vehicles include:  
* Safety and Data protection: Autonomous vehicles generate and process vast amounts of data, including sensor readings, location information, and communication data. Protecting these data from unauthorized access or manipulation is essential to maintain privacy and prevent misuse. Ensuring the security of these systems is vital to prevent unauthorized access or malicious attacks that could compromise the safety of the vehicle and its occupants [179]. * System integrity: Autonomous vehicles rely on complex software and hardware systems to operate effectively. By adhering to security principles such as secure coding practices and regular vulnerability assessments, the integrity of these systems can be maintained, reducing the risk of system failures or malfunctions [180]. * Resilience to attacks: Autonomous vehicles are potential targets for cyber attacks, which can range from unauthorized access to the vehicle's systems to remote control of its functions. Implementing security principles helps to identify vulnerabilities, establish robust defenses, and develop incident response plans to mitigate the impact of attacks and ensure the vehicle's continued operation [180]. * Public trust: Security principles are essential for building and maintaining public trust in autonomous vehicles. By implementing robust security measures, autonomous vehicle manufacturers and operators demonstrate their commitment to protecting the safety and privacy of users [179]. * Perception security: Autonomous vehicles heavily rely on perception, such as obstacle detection, traffic sign detection, lane detection, etc. With the power of deep learning algorithms, such perception tasks in autonomous driving systems widely apply deep neural network (DNN)-based models. Recent works have found that DNN models are generally vulnerable to adversarial examples or adversarial attacks. Thus, studying the security of perception in autonomous driving systems under physical-world adversarial attacks is very necessary [181]. * Data poisoning attack: The development of connected and autonomous vehicles (CAVs) relies heavily on deep learning technology, which has been widely applied to perform a variety of tasks in CAVs. On the other hand, deep learning faces some security concerns. Data poisoning attacks, as one of the security threats, can compromise deep learning models by injecting poisoned training samples. Therefore, the principles of poisoning attacks are worth studying in order to propose countermeasures [182]. * Intrusion detection: Intelligent transportation systems (ITSs), particularly autonomous vehicles (AVs), are susceptible to safety and security concerns that impact in people's lives. The safekeeping of communications and computing constituents of AVs can be threatened using sophisticated hacking techniques, consequently disrupting AVs from operative usage in our daily life routines. In this regard, a multistage intrusion detection framework can be used to identify intrusions from ITSs and produce a low rate of false alarms. The proposed framework can automatically distinguish intrusions in real time [183]. These principles are important for protecting the security of autonomous vehicles and the dangers they seek to counteract. Autonomous vehicles rely on complex software and hardware systems to operate, and any vulnerabilities in these systems can be exploited by attackers to gain control of the vehicle or cause it to malfunction.