## 4 Experiments and Results: Performance Analysis
### Simulation Results
Moreover, we studied the resistance against wormhole attacks for networks with different vehicle speeds. Figures 15 and 16 demonstrate the verification probability and probabilistic possibilities depending on the trust factor, and past vehicular interactions were examined by authenticating networks. Figure 12: Wormhole attack probability over varying network densities. Figure 13: Falsification attack probability over varying network densities. As illustrated in Figure 17, the graph showcases the relationship between varying vehicle speeds and the corresponding probabilities of successful verification. The x axis enumerates a spectrum of vehicle speeds, while the y axis quantifies the probability of successful verification. Two methodologies were evaluated: a basic approach and a proposed method. The plot reveals that the proposed method consistently outperformed the basic method across a wide array of vehicle speeds. This superior performance is manifest in the higher probability values associated with the proposed method, as depicted by the 'x'-marked  
Figure 14: Packet-drop attack probability over varying network densities. Figure 15: Probability situations to measure the vehicle’s authentication. line on the graph. Such observations substantiate the efficacy of the proposed method in high-velocity vehicular scenarios. As depicted in Figure 18, the graph illustrates the impact of node mobility on the probability of maximal verification using both the basic and proposed methods. The x axis represents the range of node mobility, while the y axis indicates the corresponding probabilities for successful maximal verification. Figure 16: The impact of high node mobility and maximum network density on the verification process. Figure 17: Verification probability over varying vehicle speeds. It is evident that the proposed method consistently outperformed the basic approach across various levels of node mobility. This is particularly highlighted by the higher probabilities associated with the proposed method, which are marked by 'x' on the line graph. Such a trend suggests that the proposed method is more reliable in environments with high node mobility. In comparison to MN predictions, the suggested framework provided 86 percent accuracy, which can be increased if the experiment is repeated over different network scenarios and more extended periods. Therefore, compared to existing systems, the measurement variables in the proposed methodology perform more effectively. The Figure 19 demonstrates how the efficiency of the network approached the target of 86% as malicious nodes were identified and removed. The trust rating also evolved, thereby decreasing due to the presence of malicious nodes but recovering as they were removed. As shown in Figure 19, the efficiency of the network gradually reached its target value of 86% as malicious nodes were successfully identified and eliminated. Concurrently, the trust rating within the network evolved, thereby reflecting the ongoing efforts to neutralize malicious activities. The identification of MNs is predicated on trust, with the removal of discovered MNs having no negative impact on the performance of other nodes. The suggested mechanism evaluates the trustworthiness of all other nodes in the network at regular intervals, and nodes that are affected and operate maliciously will have a poor rating and trust due to a high packet-drop rate, wormholes, and falsification attacks, but they will eventually be recognized in the long term. As shown in Figure 15, the suggested scheme had a lower packet loss ratio than the existing methodology. The reason for this enhancement is increased transparency between nodes that monitor the actions of neighboring nodes. Figure 15 depicts the improved performance against wormhole and falsification attacks. The use of blockchain technology records the specifics of each node's activity, which eliminates the possibility of editing or altering any data during transfer from one node to the next.