## 5 Autonomous Vehicle Vulnerabilities
### Countermeasures for Autonomous Vehicles
By implementing these measures,manufacturers and researchers aim to minimize the risks associated with autonomous vehicle cyber security and enhance the overall security posture of these vehicles. Current Status and Future Directions of Sensor Security in Autonomous Vehicles  
As the complexity and ubiquity of autonomous vehicles (AVs) continue to increase, the current state of their sensor security and future directions for research become critical topics. The current landscape of AV sensor security involves a plethora of techniques developed to counteract the broad spectrum of potential threats. These techniques encompass a multitude of disciplines, such as encryption, anomaly detection, intrusion detection systems, and secure communication protocols. The field of encryption, for example, is being utilized to protect data in transit from sensors to the vehicles' central processing units and cloud-based systems. Secure communication protocols, such as transport layer security (TLS) and Internet protocol security (IPSec), are being employed to ensure that data communicated between vehicles and infrastructure are authenticated and encrypted. Moreover, techniques from the realm of machine learning and data science are being incorporated to develop sophisticated anomaly detection systems that can identify unusual patterns in sensor data that are indicative of a potential attack. Despite these advancements, the industry faces considerable challenges that need to be addressed to increase AVs' resilience against threats. First, due to the complex nature of AVs and the environments they operate within, creating a comprehensive and foolproof security solution is inherently difficult. Additionally, the rapidly evolving nature of cyber threats necessitates constant vigilance and adaptation in the field of AV sensor security. The future of AV sensor security research holds promising directions. Enhancing machine learning algorithms to detect subtle and complex attacks, developing more sophisticated encryption techniques, and integrating blockchain technology for secure data recording and transaction are some of the notable future directions. However, a critical part of the future lies in developing security measures that are proactive rather than reactive, preventing attacks before they occur rather than responding to them post event. Furthermore, it is essential to ensure that security measures do not impede the functionality and efficiency of AVs. Achieving this balance will require advancements in both hardware and software, necessitating ongoing research and development. In summary, the current status of sensor security in AVs presents a dynamic landscape fraught with challenges and opportunities. The future is ripe for research that addresses these challenges head-on, advancing the safety and security of AVs. Vulnerabilities of Deep Neural Networks in the Face of Adversarial Machine Learning: Implications for Autonomous Vehicles  
The field of adversarial machine learning has demonstrated that it is possible to exploit the weaknesses that are built into deep neural networks (DNNs). This potential has been confirmed in state-of-the-art research. The first adversarial machine learning attacks on DNNs were proven in a significant study that was conducted by [PERSON] et al. [95]. During the experimental phase, the researchers introduced the idea of adversarial instances, which are small modifications made to the input images. These modifications have the potential to influence DNNs and result in inaccurate categorization. This method, which is based on a gradient-based attack, devises adversarial instances that are only slightly different from the original. By tricking image classifiers with these inputs that had been deceptively manipulated, the method was successful. In response to this groundbreaking work, [PERSON] et al. [96] formulated adversarial machine learning as a min-max problem and developed an alternative gradient-based method.