# Hybrid Trust Model for Node-Centric Misbehavior Detection in Dynamic Behavior-Homogeneous Clusters
## 8 Performance Analysis
### Experiment 1: Total Trust Value Assessment at the Vehicle Node Layer  
The trust value calculation method proposed in this study is tested in a simulation by evaluating vehicle behavior based on speed, node distance, and transmission rate. The total trust values of circulating vehicles in the simulation scenario are monitored and tracked, with Figure 9 showing the changes in total trust values for five vehicles \(v_{1}\), \(v_{3}\), \(v_{7}\), \(v_{13}\), \(v_{17}\). To study the dynamic changes in vehicle trust values in medium-density traffic scenarios, 300 vehicles were set to circulate for 15 min. The total trust value of vehicles changes in real-time based on behavior. The initial trust value is set at 0.5, indicating a neutral state of neither trust nor anomaly, with subsequent trust values depending entirely on vehicle behavior. The results show that in the most trustworthy state, vehicle trust values approach 0.9, with no vehicle achieving a perfect trust score (total trust value of 1). The results reveal that vehicle \(V_{3}\) reached a trust value of 0.654 after one minute of driving, gradually increasing to 0.872, and ending the simulation with a trust value of 0.877 at \(t=10\). This indicates that vehicle \(V_{3}\) maintained good behavior throughout the simulation. Conversely, vehicle \(V_{13}\) exhibited fluctuating and declining trust values from the start of the simulation, potentially indicating malicious intent. Vehicle \(V_{17}\) showed an initial increase in trust values between 3 and 5 min, followed by a sudden drop and a \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation Area & 20 km\({}^{2}\) \\ Vehicle Speed & [0, 60] \\ Communication Range & 300 m \\ Transmission Rate & 3 Mbps \\ Cluster Head Driving Mode & Constant speed mode \\ Vehicle Driving Mode & Krauss vehicle following model \\ Speed Standard Deviation & \(\pm\)30\% \\ Number of Vehicle Nodes & 100, 200, 300, 400, 500 \\ Vehicle Density & 0.05, 0.10, 0.15, 0.20, 0.25 \\ Bandwidth & 10 MHz \\ Communication Protocol & IEEE 802.11p \\ Modulation & Quadrature Phase Shift Keying (QPSK) \\ Signal-to-Noise Ratio (SNR) & 20 dB \\ Data Rate & 3 Mbps \\ Vehicle Communication Range & 100 m \\ Signal Attenuation Model & Nakagami model \\ Simulation Map & GSM \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameter settings. Figure 9: Vehicle node layer simulation result. subsequent continuous rise, suggesting an initial good performance, followed by a special event (such as emergency braking), and eventually a recovery to good trust status. The experiment also found that the calculation of mixed trust values requires the collection of indirect trust feedback from different neighbors. Therefore, when a vehicle exhibits malicious behavior, indirect trust feedback from multiple neighbors can quickly identify malicious actions in the trust value update process, reflecting in the total trust value calculation at the vehicle node layer. The total trust value decreases rapidly, aligning with the design goal of the trust model: malicious behavior leads to a quick drop in trust value, while trust value improvement is a slow process. Thus, trust values can reflect changes in vehicle behavior at specific stages. Experiment 2: Impact of Information Interaction on the Hybrid Trust Model in Different Traffic Densities  
To study the impact of traffic density on the mixed trust model, Experiment 2 conducted experiments from two aspects: the performance differences of the mixed trust model under different densities and the impact of traffic density on the total trust value of vehicles. 1.