# A New Individual-Based Model to Simulate Malware Propagation in Wireless Sensor Networks
## 1 Introduction  
A Wireless Sensor Network (WSN) is a group of intelligent sensors with a wireless communications infrastructure designed to monitor environmental conditions. Sensors send collected data to the primary server. This technology is the cornerstone of the Internet of Things (IoT) and the Industry 4.0 [1]. The WSN is deployed in a region of interest. Usually, WSN provide large-scale, low-cost tracking and monitors solutions due to their low power consumption (see Reference [2]). However, the nodes have a short range radius and a small coverage area. This technological infrastructure has applications in different areas such as military, industry, environment, daily life, healthcare or multimedia [3]. Recently, an IBM report [4] has published a shell injection vulnerability that can be exploited in the gateway. Commonly, these gateways are used in WSN to communicate sensors with cloud services. The company that has created these devices has been developed the fix updates before the IBM report was published. These networks can manage sensitive information and operate in hostile and unattended environments; because of this, robust security measures need to be considered in the network design. However, computational constraints of nodes, limited space for data storage, battery power supply, the unreliable communication channel and unattended operations are significant obstacles to the application of cybersecurity techniques in a WSN [5]. These limitations are the reason why studying the malware spreading over a WSN has been a growing interest in recent years. In addition, as smart devices as smartphones play an important role in the management of WSNs, also novel techniques for malware detection has appeared (see, for example, Reference [6])  
In this sense, several mathematical models have been developed based on the epidemiological mathematical model of [PERSON] [7]. These are global models where the connection topology is modelled using a complete graph (that is, it is supposed that all nodes are in contact withall nodes at every time). Recent examples of topology-based model are a classic SI model [8] that only considers susceptible (\(S\)) and infected (\(I\)) states, and a modified SIRD epidemic propagation model [9] which takes into account susceptible and infected states, also recovered (\(R\)) and damage (\(D\)). These models only consider the connection topology, but do not consider the particular characteristics of the other components of the WSN. These drawbacks can be overcome using individual-based models since this paradigm takes into account the individual connections and characteristics of the nodes, for example, the modified SEIR-V [10] and SIRD [11] epidemic propagation models, where other considered states are exposed (\(E\)), vaccinated (\(V\)) and dead (\(D\)). Agent-Based Models (ABM) are models where individuals (also known as agents or actors) are defined as unique and autonomous entities, interacting with each other and with their local environment. An ABM is composed of agents, environments and rules [12]; where different types of agents represent individuals within the simulated system. This paradigm is commonly used for systems with heterogeneous, autonomous and proactive agents where individual characteristics cannot be ignored. For example, these models are often used in scientific disciplines such as health [12], social environment [13], archaeology [14], economics [15] or ecological systems [16]. The design of an ABM has significant advantages such as the consideration of the node-node and node-environment interaction. The transition rules can be adjusted with real parameters of interest. Another advantage is that we can analyse and monitor the behaviour of a specific node or the network. However, a disadvantage of ABM simulation is that large-scale network simulations require more computational resources than simulating a small network. In this way, the simulation software can limit the number of nodes in the network.