# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 3 Trust Model Concepts
### Trust Model Structure  
For the purposes of this analysis of trust models, a high-level block diagram structure of a trust model was contrived, as seen in Figure 1. It is important to note that this is not based on any particular model and by no means defines all trust models. Many models loosely follow this structure, potentially leaving out or adding in components to the structure to suit their specific applications. This high-level block diagram includes the most common and pivotal features of trust models and aims to provide an understanding as to how a trust model typically operates. What follows is a description of each section numbered in the diagram. **(1) Data gathering. ** The data gathering phase is essential to every trust model, as there must be data in order to calculate trust. The data gathering phase mainly collects data from three groups (just one or a combination of multiple groups can be used). These groups are shown in Figure 1.  
Physical data include data such as energy and resource consumption of the device, the device status (software/OS version) and packet characteristics. Essentially, this includes any physical data about the device. These data can be used to detect odd behavior or anomalies in the device and can also be further used to determine direct and indirect trust. Direct trust refers to data obtained from interactions between the trustee and trustor. This type of data can be obtained through monitoring the communication behavior of, or the packets being sent by, a device, or through other physical data that is obtained, or by another method of defining whether the interaction was successful or not. It is often determined through probabilistic means, based on the communication behavior of the device. Indirect or neighbor trust is essentially direct trust obtained from another source. For example, if node A wishes to communicate with node B, node B can calculate its own direct trust with A, but also ask nodes C and D for the direct trust values between themselves and A. B can then take this value into account when calculating its trust of A. Usually, the indirect trust is accumulated from all nearby neighbors and summed or averaged in some of them, and this is taken into account with less weight of effect than the direct trust values. **(2) Data processing. ** In some models, the data are pre-processed before being used in the trust calculation. In these types of models, the data are pre-processed using a mathematical model individually, and the trust calculation acts as a form of integrating  
Figure 1: Diagram of a trust model’s basic structure. these values together, such as a weighted average or sum. This is not a necessary step; however, it can be useful in some instances. For example, direct trust can be calculated using physical data or potentially by considering previous trust values or more than one parameter. Multiple indirect trust values are obtained from neighbors and need to be amalgamated. These calculations can take place before the trust calculation, if necessary, depending on the model being used. **(3) Trust calculation**. This phase is at the heart of the trust model. It is where all the data obtained are converted to a meaningful trust value to determine whether the device/node is malicious or not. This calculation can be performed in numerous ways, using different mathematical models. These models are discussed in further detail in Section 6. From the block diagram in Figure 1, we can see that all the data gathered are used in this phase, and previous trust values can also be fed back and used in this phase. The trust value that is calculated in this phase is updated regularly, and this can be accomplished through time- or event-triggered updating, depending on the model. **(4) Historical trust values**. Many trust models, but not all models, take into account historical trust values when calculating their trust.