## 5 Simulation Results  
To check the results of our system, we built a C#.net Windows Forms application, and used big data to check all the criteria applied to avoid these attacks. Experiments were used to determine the trust model's capability of resisting reputation attacks. The penalties for the on/off attack were determined by the TMS based on two conditions: whether the interaction importance was more than or equal to the danger rate \((DR)\), and whether the feedback \((F)\) of the recommender was less than the interaction importance \((II)\). When the recommender's feedback \((F)\) was lower than the interaction importance \((II)\), the trustdecline penalty \(\left(P^{TD}\right)\) was determined by the trust model. The effect of the penalty of on/off attack and trust decline in the interaction trust values are illustrated in Figure 2.  
The value of interaction trust for malicious users is determined by the trust model by using the penalties of malicious behavior. The effect of new feedback on the interaction trust for malicious users and trusted users is demonstrated in Figures 3 and 4. Figure 3: Interaction trust values for 100 trusted consumers. Figure 2: Penalties of on/off attack and trust decline. The trust model's ability to endure reputation attacks is examined in this section by carrying out experiments. The collusion attack frequency (\(\mathrm{CAF}\)) was calculated by the TMS to prevent collusion attacks. Here, the feedback frequency had a direct relationship with feedback collusion and an inverse relationship with the feedback's credibility. The feedback frequency was determined by the number of recommender feedback items and the total number of feedback items within the suspected set (\(\mathrm{SS}\)). The feedback frequency of seven suspected recommenders is presented in Figure 5 and as we assume in Table 1. It can be seen that the feedback frequency of five suspected recommenders was more than the feedback limit (\(\mathrm{FL}\)), which implies that the TMS will transfer the feedback given by these recommenders to the collusion set (\(\mathrm{CS}\)), or the suspected feedback by a particular recommender for a particular user will be shifted by the trust model to the feedback set (\(\mathrm{FS}\)). Figure 4: Interaction trust values for 100 malicious consumers. Figure 5: Collusion attack frequency. Figure 6 shows how the trust model is used to determine the attack scale \((\mathrm{AS})\), which refers to the size of attack scale for various collusion sets. This is done to find out the size of the collusion set (CS) in which the malicious recommenders in a recommender's community consist of a significant percentage of all recommenders to attack and destroy the trust model. The value of the attack target scale \((\mathrm{ATS})\), which gives the malicious feedback rate from one collusion set \((\mathrm{CS})\) for a particular user, is illustrated in Figure 7. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline FN\((\mathrm{SR}_{i},\mathrm{CR})\) & 6 & 21 & 32 & 12 & 36 & 1 & 18 \\ \hline FN\((\mathrm{SS},\mathrm{CR})\) & 126 & 126 & 126 & 126 & 126 & 126 & 126 \\ \hline CAF\((\mathrm{SR}_{i},\mathrm{CR})\) & 0.05 & 0.17 & 0.25 & 0.10 & 0.29 & 0.01 & 0.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Collusion attack frequency. Figure 6: Attack scale \((\mathrm{AS})\). Figure 7: Attack target scale \((\mathrm{ATS})\). The value of the collusion attack strength (CAS) determined by computing the rate of all malicious feedback from distinct collusion sets (CS) for a given user is illustrated in Figure 8.  
Comparison of Security and AccuracyAny type of trust management service is vulnerable to a variety of attacks [31]. These attacks have the potential to either enhance or destroy the reputation of a specific entity [32; 33]. In order to build an accurate and secure trust model system, we focus on implementing various metrics to prevent these attacks, which enables us to create a stable, reliable, and accurate trust model framework.