## 5 Fuzzy Logic Approaches in Trust Management
#### 5.1.5 Mobile Network
proposed a fuzzy comprehensive evaluation to assess trust among nodes in software-defined networks (SDNs). This method uses fuzzy logic to handle the complexity of node behavior, allowing for a more nuanced evaluation of trust attributes. TrustBlock also improves trust calculation by calculating trust values from direct, indirect, and historical perspectives. It uses the entropy method for objectivity and resilience to periodic attacks [46]. This study uses blockchain technology to evaluate trust levels in SDN nodes, thus enhancing the integrity and controllability of trust evaluation. The double-layer blockchain architecture is used for identity authentication, trust-value storage, consensus mechanisms, and trust-value tracking. Blockchain ensures data authenticity, irreparability, and openness, whereas the consensus mechanism filters malicious recommendation nodes and preventscolluding attacks. The immutability of blockchains allows for the tracking of historical trust data and prevents rapid fluctuations. This robust and secure framework addresses both the reliability and security aspects of trust assessments in dynamic network environments. The TrustBlock model achieves high detection rates of up to 98.89%, an accuracy rate of 98%, and enhanced security and reliability by utilizing a double-layer blockchain architecture [46]. [PERSON] et al. proposed a fuzzy trust mechanism that calculates trust values based on similarity, correlation, and user interaction. The mechanism uses a fuzzy comprehensive evaluation algorithm to compute fuzzy trust, considering multi-dimensional features and trust levels. The mechanism achieves personalized privacy without compromising the usability of mobile social applications. The paper also includes theoretical analysis and simulation experiments to demonstrate its effectiveness in addressing privacy concerns and providing users with control over their sensitive information [47]. [PERSON] et al. proposed a fuzzy-based trust management system to address security and trust challenges in 5G networks. The system evaluates device trustworthiness using factors like behavior, reputation, and context. This approach handles uncertainty and imprecision in data, improving the system's accuracy and effectiveness in preventing Sybil attacks [48]. The framework successfully established trust between OpenFlow controllers and network management apps, identified malicious feedback, and introduced a dynamic trust model for Internet of Things scenarios with 89-95 percent success rates. [PERSON] et al. proposed a combination of fuzzy logic theory and generative adversarial learning to develop an intelligent trust management framework for 6G wireless networks. The methodology involved fuzzification to convert input trust attributes into type-2 fuzzy sets, followed by inference type reduction and defuzzification to obtain the final trust value. A dataset was prepared to train the GAN-based autoencoder, which used feature extraction, data labeling, and trust vector creation. The outcomes and enhancements demonstrated, which incorporated AI-based methods and cutting-edge trust assessment mechanisms, can increase security, resilience, and trust management in 6G wireless networks [49]. In addition to the fuzzy logic algorithm, this study uses Generative Adversarial Networks (GANs) for trust evaluation in 6G networks. GANs are a deep learning framework with two neural networks, the generator and the discriminator, trained simultaneously. The method enhances trust decision making by training autoencoders on trust-related data and synthesizing additional samples. This approach provides a comprehensive and adaptive approach to trust management in dynamic networks [49].