# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 5 Experimental Analysis
### Reliability, Anti-Attack Capability, and Latency
Referring to the method in [48], the true positive rate (\(TPR\)) and the true negative rate (\(TNR\)) are used as comparison performance indicators, respectively, and are defined as follows. \[TPR=\frac{\#\,of\,real\,\,normal\,\,node}{total\,\,\#\,of\,\,normal\,\,node \,\,detected} \tag{21}\]  
\[TNR=\frac{\#\,of\,\,real\,\,malicious\,\,node}{total\,\,\#\,of\,\,malicious\,\,node\,\,detected} \tag{22}\]  
We use AATMS [15], BTDS [16], and hybrid [29] as the comparison model. Hybrid considers the interaction trust and social trust of the nodes based on the VSN, but does not consider the influence of the historical reputation. AATM does not take into account the social factors. In the simulation, we set the number of vehicle nodes to 200, the initial reputation of all vehicles is set to 0.6, and the iteration rounds to 300. In each round, 10 vehicles were randomly selected as event reporters, and we randomly assign 19 cooperative certification vehicles to each event. Figures 7-9 show the performance of the schemes under direct attack, on-off attack, and collusive attack. Figure 7 shows the TPR and TNR of four schemes with different percentages of direct attack. Compared to the other three schemes, the DBREMM performs better in the comparison of TPR and TNR, especially in the case with fewer malicious nodes. Due to the attenuation factor in indirect trust computing and the threshold control of vehicle quantity and report quality in the fusion of trust, malicious vehicles can hardly damage the network under the DBREMM. In Figure 8, the same result is obtained for the on-off attack (hybrid does not involve the defense against on-off attacks), which further verifies the anti-attack capability of the DBREMM. Figure 9 shows the TPR and TNR of two schemes with different percentages of collusive attack (BTDS and hybrid do not involve a defense against a collusive attack). The DBREMM considers trust convergence in trust fusion and sets the defense threshold. Therefore, it can be seen that when the percentage of collusive nodes is 10-30%, the system is basically unable to be attacked. When the collusive node proportion is 30%, the performance of AATMS begins to decline, while the DBREMM can still maintain a good performance. The TNR of both schemes decreases when the proportion reaches 40%. However, in the follow-up of the DBREMM, the MPoR consensus protocol of our previous work is adopted. Through consensus, the defense against a collusive attack can be completed, which can compensate for the shortcomings of the reputation update scheme. Figure 7. Performance of four schemes under direct attack. (**a**) TPR and (**b**) TNR. The performance and simulation of the MPoR consensus protocol have been discussed in [47]. Finally, the latency of the DBREMM reputation evaluation processes and consensus are given in Table 3. The vehicle nodes reach from 0 to 200. It can be seen that the total latency of the DBREMM is maintained at the order of 0.1\(s\), which can basically meet the demand for high efficiency in VANET.