## 3 Results
### Experiment and Analysis
#### 3.2.2 Verify the Accuracy of LFCTEM by Comparison  
Two important indexes for analyzing the accuracy of trust models of edge devices are detection rate and error detection rate of malicious devices. The detection rate is the ratio of the number of detected malicious edge devices to the total number of malicious edge  
Figure 8: Comparison of incentive score with different negative collaboration time(red: positive, blue: negative): **(a)** Edge devices that passively collaborate for 6 h; **(b)** Edge devices that passively collaborate for 12 h; **(c)** Edge devices that passively collaborate for 16 h; **(d)** Edge devices that passively collaborate for 20 h.  
devices according to the trust evaluation scheme of the model. Error detection rate is the ratio of the number of false detected edge devices according to the trust evaluation scheme of the model (that is, a normal edge device that is mistakenly detected as a malicious edge device, or a malicious edge device that is mistakenly detected as a normal edge device) to the total number of detected edge devices. In this experiment, the detection rate and error detection rate of malicious edge devices in the CEEC network of LFCTEM, DTEM [30] and FITEEV [18] are compared and analyzed at 5%, 10%, 20%, 40% and 50% of malicious edge devices. Among them, DTEM is a trust management system that adds time degradation factor and incentive mechanism. It modifies Bayes' equation by satisfaction function to solve direct trust, and determines the weight of indirect trust value based on improved grey correlation analysis. FITEEV is a reputation data-management system based on fuzzy logic. It introduces packet forwarding rate factor, correctness factor, monitoring factor, speed factor and content change factor to improve the detection rate of malicious devices. Figures 9 and 10 show the detection rate and error detection rate of the three models under different proportions of malicious side devices. As shown in Figure 9, the detection rate of LFCTEM is significantly higher than that of the other two models, and the detection rates of the first four groups of malicious edge devices with different ratios are all higher than 80%. In the evaluation of trust value, LFCTEM converts the fine-grained trust information of the edge devices group, including success rate of direct interaction, adjacent distance, success rate of public interaction and positivity of cooperation evaluation, into fuzzy trust factors based on the fuzzy logic algorithm so as to further enhance the accuracy of the trust evaluation between the adjacent edges devices, and enhance the malicious edge devices detection rate of the model. As the proportion of malicious edge devices in the CEEC network increases, the detection rates of malicious edge devices of the three algorithms all decrease. However, LFCTEM always keeps a high detection rate of malicious edge devices and the detection rate of malicious  
Figure 9: Comparison of detection rates of malicious edge devices by LFCTEM, DTEM and FITEEV. edge devices of LFCTEM increases by 19.11%, so it can be seen that LFCTEM has a better performance than the other two models. As shown in Figure 10, the error detection rate increases with the proportion of malicious edge devices. However, the average first four error detection rates of LFCTEM, DTEM and FITEEV models are 13.96%, 27.85% and 18.85%, respectively, and LFCTEM has the lowest false detection rate. Through the analysis, it can be seen that the incentive mechanism is introduced in LFCTEM to reward the accuracy of cooperative trust evaluation of the edge device group using quality reward scores. This is in order to avoid the impact of malicious trust evaluation and false evaluation caused by malicious behavior of edge devices, and, thus, reduce the error detection rate of edge devices.