# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
###### Abstract  
Over the past decade, new technologies have driven the rise of what is being termed as the fourth industrial revolution. The introduction of this new revolution is amalgamating the cyber and physical worlds, bringing with it many benefits, such as the advent of industry 4.0, the internet of things, cloud technologies and smart homes and cities. These new and exciting areas are poised to have significant advantages for society; they can increase the efficiency of many systems and increase the quality of life of people. However, these emerging technologies can potentially have downsides, if used incorrectly or maliciously by bad entities. The rise of the widespread use of sensor networks to allow the mentioned systems to function has brought with it many security vulnerabilities that conventional \"hard security\" measures cannot mitigate. It is for this reason that a new \"soft security\" approach is being taken in conjunction with the conventional security means. Trust models offer an efficient way of mitigating the threats posed by malicious entities in networks that conventional security methods may not be able to combat. This paper discusses the general structure of a trust model, the environments they are used in and the attack types they are used to defend against. The work aims to provide a comprehensive review of the wide assortment of trust parameters and methods used in trust models. The work discusses which environments and network types each of these parameters and calculation methods would be suited to. Finally, a design study is provided to demonstrate how a trust model design will differ between two different industry 4.0 networks. trust; trust model; security; networks +
Footnote †: journal: Sensor and Actuator Networks  
networks, are operating on the basis that nodes may enter and leave the network with relative ease, allowing the nodes to send and receive data within the network. In the case of WSNs (wireless sensor networks), many of the nodes will not have high computing capabilities and can be quite vulnerable to cyber-attacks. Due to the potential openness of their deployed environment and transmission medium, the nodes could also be vulnerable to physical attacks, such as tampering or hijacking attacks [3]. All these attacks are internal to the network and cannot be conveniently mitigated by conventional means of security, such as cryptography and authentication. Trust models are a simple concept used to protect against the aforementioned internal attacks. Trust models are simply a method of interpreting the data provided by nodes in the network to determine if they are trustworthy or not. This concept is similar to, and based on, how people establish trust in their everyday lives. Humans are able to interpret large amounts of data from the world around them and use these data to make decisions, such as whether a person be trusted. For example, if you met a stranger whom you just witnessed stealing something from a shop, you would be much less likely to trust something they said than if you met a friend you had known for years, and who has never, to your knowledge, done anything bad. It is from these simple concepts that trust can also be developed in networks. Nodes that have had good previous interactions with one another will afford one another a higher level of trust than they will afford a new \"stranger\" node that appears to be sending peculiar data in the network. The data used to calculate trust can be divided into three main categories: direct trust, indirect trust and physical data. These categories are also what people use in their assessment of trust, and can be thought of in a synonymous way when applied to the cyber world. The three categories are explained very briefly below, along with analogies of how people tend to use them in their trust calculations of another person. _Direct trust:_ Your own personal, direct experience with the entity--are they a friend you have known for a while?