# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 7 Model Design Recommendations  
This section discusses the design of new trust models and makes recommendations regarding what a trust model should include. Commonly, the most intensely studied aspect of the trust model is the trust calculation itself. This calculation should effectively establish a value of trust and/or classify data successfully as untrustworthy or not. This goal can be achieved with equally high accuracy in a number of ways. The parameters measured to obtain data in order to calculate this trust are perhaps more important. Table 1 shows a comparison of some of the models analyzed in this work. Clearly, data, communications and resource usage are the most common parameters used. This is due to the fact that every network includes these parameters in some form. They are essential for the operation of the network, which makes them a big target for many of the attacks outlined in Section 4. When designing a new trust model, the parameters should be selected with attacks in mind. The attacks that the implementation of the model aims to mitigate should be \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Network Type**} & \multicolumn{6}{c}{**Measured Parameters**} & \multirow{2}{*}{**Trust Ageing**} & \multirow{2}{*}{**Dynamic Threshold**} \\ \cline{3-3} \cline{5-8}  & & & & **Interaction History** & & & **Resource Consumption** & \\ \hline
[3] & WSN & ✓ & ✓ & ✓ & & & \\ \hline
[9] & IoT/CPS & ✓ & & & & & \\ \hline
[10] & IoT/CPS & & & ✓ & & & \\ \hline
[12] & WSN/IoT & ✓ & ✓ & & ✓ & & \\ \hline
[14] & IIoT & ✓ & & & & & ✓ \\ \hline
[15] & IoT & & ✓ & & & & \\ \hline
[17] & IIoT & ✓ & ✓ & & & & \\ \hline
[19] & MCS & & ✓ & & & ✓ & \\ \hline
[20] & WSN & & ✓ & ✓ & ✓ & ✓ & \\ \hline
[22] & WSN & ✓ & & & & & \\ \hline
[23] & UASN & ✓ & ✓ & ✓ & ✓ & ✓ & \\ \hline
[24] & WSN & ✓ & ✓ & & & & \\ \hline
[25] & WSN & ✓ & ✓ & ✓ & ✓ & ✓ & \\ \hline
[27] & WSN & ✓ & & & ✓ & & \\ \hline
[28] & Underwater WSN & & ✓ & & ✓ & ✓ & \\ \hline
[29] & WSN & ✓ & ✓ & & & ✓ & \\ \hline
[30] & VANET & & ✓ & & & & \\ \hline
[31] & VANET & ✓ & ✓ & ✓ & & & \\ \hline
[32] & VANET & ✓ & ✓ & ✓ & & & \\ \hline
**Usage Rate** & 68.4\% & 73.7\% & 21\% & 31\% & 36.8\% & 5.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the attributes used in trust models. analyzed to determine what parameters of the network they will affect. Parameters behave differently, in most cases, while under attack; thus, measuring these parameters should help in detecting the attacks. For example, in a routing-type attack, the communication will be affected, and a DoS attack will affect the resource consumption. All models should dynamically calculate trust, meaning that trust should regularly be updated for the nodes within the network. Trust is not a static metric and continues to change over time and should, therefore, always be updated regularly in order to detect malicious nodes successfully. If a model is being designed with the intent of being used in more than just a single unique network, flexible design aspects should be included in the model to allow it to adapt to different networks. Machine learning models can achieve this through training on each unique network's data, or it can also be achieved by using methods such as adaptive parameter weighting [16; 22] and dynamic thresholds [3]. For the most secure results, more than just a trust model is needed on a network. Trust models alone cannot provide confidentiality or data integrity. Encryption can be used to provide confidentiality and authentication to a network, and the hashing of messages can provide data integrity. Other methods for authentication can be used, such as device fingerprinting, which is used alongside a trust model in [9]. These methods can be costly for low-powered sensor networks in terms of computation and resource usage. Lightweight key management schemes and edge cryptography techniques can be used in these scenarios [36; 37].