Energy Aware Autonomous Deployment for Mobile Wireless Sensor Networks: Cellular Automata Approach

Al-Tabbakh, Shahinaz M.; Shaaban, Eman

doi:10.1007/978-3-319-53934-8_11

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data collected by the sensor nodes are broadcasted in the form of multi-hop in the whole network. With the increasing sensitivity and value, the security of WSN is becoming more and more important [1][2][3][4]. Since the sensor nodes have weak self-protection ability and isomorphism, when a node is threatened, a large number of sensor nodes can easily be compromised by the same malware which will affect the confidentiality and the stability of WSN [5].…”

Section: Introductionmentioning

confidence: 99%

Cellular automata malware propagation model for WSN based on multi‐player evolutionary game

Wang

Dong

2018

IET Networks

View full text Add to dashboard Cite

When wireless sensor network (WSN) is running, it needs data acquisition, fusion, and transmission, which makes the attacker distribute the malware code to the network to destroy the normal operation of the network. In this study, an improved two-dimensional (2D) cellular automata model and a multi-player evolutionary game model are proposed to describe the propagation process of malware propagation, aiming at the shortcomings of research on malware propagation in WSN. First, the authors introduce the node diversity model according to the difference between the cluster-head node and the terminal node in WSN. Then, based on the existing 2D cellular automata malware model, an improved mechanism of epidemiological transmission is improved. To make the model more complete, a multi-player evolutionary game model is established according to the different attack methods and the different coping strategies adopted by the cluster-head node and the terminal node in WSN. The replication dynamic equation of the strategy evolution is also given. Simulation results show that the proposed model can accurately describe the propagation behaviour of malware in WSN and provide a basis for security defence research of WSN.

show abstract