# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 5 Trust Data Gathering
### Trust Parameters  
**Communications** are a vital part of any network and many attacks. Attacks can often change communication behaviors in a network; therefore, it makes sense to use communications as a parameter for measuring trust. Many models use some attribute of the communications between nodes to help to model trust. The message frequency and packet forwarding rate [9; 17; 32], as well as the packet forwarding behavior [24], are simple ways of determining if a node is behaving unusually. Another common approach is to analyze the ratio of successful interactions/packets sent, or that of unsuccessful or total interactions/packets sent. This type of communications trust is used widely (reference [3] terms it as honesty trust) [14; 22; 23; 25; 27; 29; 31]. This approach requires a method of defining a successful or unsuccessful transmission, which is usually achieved using a simple set of rules defined for the given context. Each model will differ in how it defines successful or unsuccessful transmissions, but a common approach is to use an ACK signal to indicate the received packets. The communication trust score is calculated using simple ratios, equations or probabilities to convert the data collected into a value that can later be used in the trust calculation. The method used to achieve this is slightly different for each model so as to suit its contextual needs. **Data** can be attacked through some of the attack methods mentioned in Section 4, and these attacks can modify and change data being sent by nodes in a detectable manner. Data checking can often be performed in simple yet effective manners, adding little computational overhead to the network. The authors of [3, 20, 24, 25, 28, 49] use simple anomaly-type checks, such as comparing sensor data to the average value of the data from all sensors. If the data is flawed, then there may be a potential compromise. The authors of [30, 31] use plausibility checks on the data, both operating on a VANET network and employing methods for determining vehicles' distance from the roadside unit that they are in communications with. If the data indicates that it is being sent from a location outside this range, these checks will catch it and instantly discard the information. These types of checks seem trivial, but they are very effective and add little computational overhead to a model and, therefore, it makes sense to include them. Other models use methods such as analyzing the expected values of the amount of data being collected [17], analyzing temporal [15] or spatial [23] correlations of the sensor data, and detecting if a packet is malicious based on its signature [12] or successful data interactions [29]. In [19], a quality of data measurement is used to develop a QoD score for nodes in a crowd-sensing network, and this value influences the trust value of the node. There are eight quality dimensions used to attain this QoD score. Six of these data qualities are defined in [50] as accuracy, completeness, reliability, consistency, uniqueness and currency. The remaining two are defined as syntactic accuracy and semantic accuracy, in the case of live sensor data. This method of QoD assessment works well for applications such as crowd sensing but could also be applicable to other applications, such as some IoT or WSNs. **Interaction history**. Often, nodes will have had previous interactions with each other. Just as people develop (or lose) trust in each other through more interactions, nodes in a network can too. This can be achieved through counting the number of previous successful or unsuccessful interactions between nodes, and by allowing nodes to be more trusting with entities they have a good history with and more cautious with entities they have bad or no history with. The authors of [3, 31, 32, 3] all consider the number of previous interactions that nodes had with each other.