## 5 Evaluation
### Safety Simulations and Feedback  
* Sybil attack scenario-The first scenario in our evaluation involved the simulation of a Sybil attack where a malicious node attempted to create 50 false identities to disrupt the network. The attacker's goal was to inject false data into the vehicle-to-vehicle (V2V) communication to manipulate and disrupt the coordination between the vehicles. By injecting these fake identities, the attacker attempted to confuse the network by generating false information about vehicle locations, traffic conditions or hazards, potentially leading to false actions by other vehicles. This type of attack can lead to significant safety risks, including traffic jams or collisions, as vehicles are misled about the actual state of the environment. Our system's blockchain-based consensus mechanism was able to successfully detect and block all 50 fake identities, preventing any manipulation of V2V communication. This result demonstrates the robustness of the system's authentication process, which relies on cryptographic identities validated via the blockchain to ensure that only legitimate vehicles can participate in the network. The system's ability to mitigate this attack with a 100% success rate while minimally impacting latency underscores its effectiveness in securing vehicle communications against Sybil attacks. * Denial of service attack scenario-in the second scenario, we simulated a denial of service (DoS) attack in which a malicious node flooded the network with 1000 fake transaction requests per second and targeted the communication between vehicles and RSUs (vehicle-to-infrastructure, or V2I) to overload the blockchain nodes. The goal of the attack was to disrupt the network by overloading the roadside units (RSUs) and preventing legitimate transactions from being processed. The system's consensus mechanism, combined with rate-limiting measures, successfully mitigated the attack by discarding 95% of malicious requests at the network level before they could reach the blockchain. The remaining 5% of requests were validated by the RSUs, identified as fake transactions, and discarded without being included in a block, ensuring that the malicious activity did not compromise the integrity of the blockchain ledger. * Mitigation rate-the system was able to neutralize 95% of attacks at the network layer, while the blockchain layer rejected 100% of forged transactions, maintaining data accuracy and security. * Impact on throughput-during the peak of the attack, the throughput of the system was reduced by 20% as resources were temporarily allocated to process and forged requests were rejected. However, no legitimate transaction was delayed by more than 50 ms, allowing communication between vehicles and RSUs to continue despite the attack. It is important to note that the scenarios described are based on simulations and not on tests with actual vehicles or infrastructure. Due to the limitations of this project, including access to real vehicle networks, we used simulation environments to model and evaluate the performance of the system. These simulations allowed us to realistically assess the behavior of the system under certain attack scenarios, including Sybil attacks and denial of service (DoS) attacks, and to measure important metrics such as mitigation rates and impact on throughput. By using simulation tools, we were able to recreate network conditions similar to those in real vehicle communication systems. While these simulations provided valuable insight into the effectiveness of the system, the results should be viewed with the understanding that real-world conditions may introduce additional complexities and variability that are not fully captured in a simulated environment. For these simulations, we implemented all components using Python in a simplified form. Python provided a flexible and efficient framework for developing and testing the key aspects of the blockchain-based secured communication system.