# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 5 Experimental Analysis
### Reliability, Anti-Attack Capability, and Latency  
In this section, firstly we will verify the accuracy of the DBREMM. Reputation needs to accumulate steadily over time, and bad behaviors can damage it quickly, that is \"slowly accumulating and rapidly declining\". The accuracy of the scheme is determined by observing the reputation updates of the normal nodes and malicious/faulty nodes, respectively. In our experiment, we simulate 100 rounds of reputation iterations for two specific nodes. One node is a normal node that continues to behave well. The other node is a direct attack node that continues to commit evil. We observe the iteration of the reputation of the two nodes in the role of event reporter and cooperative node separately. The initial reputation value is set to 0.6, and the social measure is randomly assigned with a normal distribution of \(N\sim(0,0.03)\), and it is updated after every five iterations. The comparison model is BTDS in [16], which is a trust management model that uses BI as well. The result is shown in Figure 5.  
As shown in Figure 5, for the event reporter, the reputation of the normal node accumulates more slowly than that of the BTDS scheme, while for the malicious node, the reputation declines more quickly when committing evil. By comparing a normal node and malicious node, it can be seen that the DBREMM scheme has a better precision of \"slowly accumulating and rapidly declining\". For the collaborator, the reputation changes more slowly than for the event reporters, because our scheme rewards and punishes event reporters more severely. As the scheme sets the ceiling of the accumulated reputation of the collaborative certification, the reputation of the collaborator will not rise after reaching 0.8, and cannot continue to accumulate reputation through collaborative certification. The decline in reputation is not affected by the upper threshold of the collaboration. Although the decline is slower than that of the event reporter, it always falls to the same level eventually. In the calculation of the reputation, the DBREMM considers the measurement of social factors, and sets the adjustment factor based on the time for social factors, so the simulation curve has jagged fluctuations. Then, we change the direct attack node to an off-off attack node and observe its performance under the DBREMM. The result is shown in Figure 6.  
Figure 6 shows the reputation change curves of the on-off node, acting as reporter and collaborator, respectively. The node alternately performs normally and maliciously in network activities. It can be seen that under the DBREMM, the reputation value can accumulate normally when it performs normally, but once it starts to perform maliciously, its  
Figure 4: Performance comparison of the decision logics. reputation declines more rapidly than that of BTDS. The greater the variance of reputation, the easier it is to be recognized by the network and respond in time. Meanwhile, due to the existence of a reputation weight factor in the DBREMM scheme, it will take more time and it will cost more to raise the reputation to the original level after it declines, regardless of whether it is for the reporter or collaborator. The above two simulations mainly study the accuracy of the algorithm. In the actual application scenarios, when reputation accumulates or decreases to a preset value, different network behaviors will be triggered, including to convert the greater reputation into circulating currency and reset, or the node will be kicked out of the network. The main objective of the above experiments is to check whether the changes in the reputation of various types of nodes in the network can well reflect the behaviors of the nodes. Therefore, it can be seen that our scheme is feasible. Figure 5: Reputation update for continuous good/malicious behavior. Figure 6: Reputation update in on-off attacks. Moreover, to study the overall performance of the scheme, we verify the anti-attack capability of three types of malicious attacks mentioned in Section 3.2.