# A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios
## 2 Related Works  
The field of logic-based approaches for trust and reputation has garnered growing interest, leading to the development of several innovative methods. For instance, in [13], the authors propose formal definitions of various types of trust within a modal logic framework, describing trust as a \"mental attitude of an agent\" concerning certain properties (epistemic, deontic, and dynamic) attributed to other agents. In [14], the focus shifts to reasoning about quantitative aspects, such as trust levels, through the introduction of \(TCTL^{G}\), a logical language tailored to represent this information. The authors also created a symbolic model-checking algorithm to quantify relationships among agents. Our approach, in contrast, employs the T-pattern model, which, while reminiscent of logical formalism, relies on a unique form of quantifying rule strength rather than logic predicates. Various studies in the literature leverage machine learning (ML) to address trust issues [15; 16; 17; 18]. This alignment fits well with the T-pattern model, as its multi-agent framework inherently supports knowledge learning. Additionally, logic-based neural networks (e.g., _if-then_ constructs [12; 19]) can be utilized to extract insights effectively from T-pattern models. Recent research has delved into machine learning and neural network approaches within human-object trust relationships. For instance, Ref. [20] investigated trust assessment in Trust Social Networks (TSNs), considering trust propagation and fusion factors, and proposed the NeuralWalk algorithm for estimating trust factors and predicting relationships. The authors demonstrated that WalkNet, a neural network model for single-hop trust propagation, could inductively predict unknown trust relations using real-world data. The work presented in [21] addressed trust in the Social Internet of Things (SIoT), where IoT devices interact in a \"social manner\", making the establishment of trust relationships essential. The authors developed an artificial neural network-based trust framework named \"Trust-SIoT\", which aims to classify trustworthy objects by identifying complex relationships between inputs and outputs. They highlighted that Trust-SIoT effectively captures various key trust metrics and demonstrated its strong performance through experimental validation within SIoT contexts. Another noteworthy study discussed in [22] focused on the Internet of Medical Things (IoMT), which aims to enhance the accuracy, reliability, and efficiency of healthcare platforms. The proposed approach, known as NeuroTrust, utilized artificial neural networks to evaluate trust parameters, such as reliability and compatibility, to predict and eliminate malicious nodes that may compromise data integrity. Additionally, a lightweight encryption mechanism is incorporated to strengthen security during data transmission. The experimental results showcased the framework's effectiveness in detecting malicious and compromised nodes, which is crucial for mitigating security threats. In [17], the authors addressed the issue of compromised or malicious IoT devices. Evaluating trust for these devices is challenging due to the difficulty in measuring various types of trust properties and the associated degrees of belief. To address this, the authors proposed a machine-learning-based trust evaluation method that aggregates network QoS (Quality of Service) properties. A deep learning algorithm was employed to create a behavioral model for each IoT device, quantifying trust as a numerical value by calculating the similarity between observed network behaviors and those predicted by the model. The study in [23] introduced an innovative intrusion detection method termed the \"Taylor-spider monkey optimization-based deep belief network\" (Taylor-SMO-based DBN). This approach incorporates trust factors for intrusion detection, utilizing an optimization algorithm that integrates the Taylor series with the spider monkey optimization (SMO) technique to classify KDD features.