## 5 Autonomous Vehicle Vulnerabilities
### Countermeasures for Autonomous Vehicles  
The classification of various types of attacks on autonomous vehicles and their respective countermeasures are shown in Table 2. In this table, we emphasize the scope and complexity of the threats that autonomous vehicles face. Although significant strideshave been made in countering some types of attacks, a number of vulnerabilities, such as those associated with OBD port tampering, man-in-the-middle attacks on the CAN bus, and replay attacks by insider adversaries, remain unaddressed. A comprehensive understanding and solution for these vulnerabilities are crucial to realize the full potential of emotionally intelligent autonomous vehicles. Autonomous vehicles (AVs) introduce numerous unique security challenges that have the potential to create safety consequences on the road [86]. Therefore, security measures are paramount to the implementation of AV networks [87]. Here are some countermeasures being developed and deployed to solve the security flaws of autonomous vehicles:  
* Game-theory-based solutions: Game-theory-based solutions could offer resilience in the context of AVs [88]. Game theory is a mathematical framework for modelling decision-making in situations where multiple parties have conflicting interests. It can be used to model the behavior of attackers and defenders in a cyber security context and to develop optimal strategies for both parties [88]. * Benchmarking frameworks: AVs lack a proper benchmarking framework to evaluate attack and defense mechanisms and quantify safety measures [86]. BenchAV is a security benchmark suite and evaluation framework for AVs that addresses current limitations and pressing challenges of AD security. The benchmark suite contains 12 security and performance metrics and an evaluation framework that automates the metric collection process using the Carla simulator and robot operating system (ROS) [86]. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Attack Type** & **Specific Attack** & **Proposed Countermeasures** & **Ref.** & **Mitigation Status** \\ \hline \multirow{8}{*}{Data Authenticity} & \multirow{3}{*}{Data Authenticity} & Signal strength monitoring & [28] & Partially mitigated \\ \cline{3-4}  & & Military-grade cryptography & [28] & Partially mitigated \\ \cline{2-4}  & & Antispoofing methods & [29] & Partially mitigated \\ \cline{2-4}  & \multirow{3}{*}{Man-In-The-Middle} & Cryptography on CAN & [44] & Not addressed \\ \cline{3-4}  & & Secondary measurement source & [44] & Not addressed \\ \cline{3-4}  & & GPS jamming & [28] & Partially mitigated \\ \cline{3-4}  & & Antijamming methods & [29] & Partially mitigated \\ \cline{3-4}  & & LiDAR jamming & [31] & Fully mitigated \\ \cline{3-4}  & \multirow{3}{*}{Data Availability} & Use of alternate data sources & [31] & Fully mitigated \\ \cline{3-4}  & & Camera blinding & [31] & Fully mitigated \\ \cline{3-4}  & & Malware attack & [83] & Partially mitigated \\ \cline{3-4}  & & DoS/DDoS attack & [60,61] & Fully mitigated \\ \cline{3-4}  & & Replay attack on LiDAR & [31] & Fully mitigated \\ \cline{3-4}  & & Auto control confusion & [31] & Fully mitigated \\ \cline{3-4}  & & OBD port tampering & [84] & Not addressed \\ \cline{3-4}  & & Exploitation and injection in CAN bus & [79,85] & Not addressed \\ \cline{3-4}  & & Masquerading attack & [64] & Fully mitigated \\ \cline{3-4}  & & Replay attack (outsider adversary) & [39] & Fully mitigated \\ \cline{3-4}  & & Replay attack (insider adversary) & [65] & Not addressed \\ \hline \multirow{3}{*}{Data Confidentiality} & Eavesdropping & Cryptographic solution (group signatures) & [66] & Partially mitigated \\ \cline{1-1} \cline{2-4}  & Eavesdropping & Cryptographic solution (short-term certificates) & [67] & Partially mitigated \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data Security attacks and mitigation strategies in autonomous vehicles.