Abstract
Currently, zero-day malware is a major problem as long as these specimens are a serious cyber threat. Most of the efforts are focused on designing efficient algorithms and methodologies to detect this type of malware; unfortunately models to simulate its behavior are not well studied. The main goal of this work is to introduce a new individual-based model to simulate zero-day malware propagation. It is a compartmental model where susceptible, infectious and attacked devices are considered. Its dynamics is governed by means of a cellular automaton whose local functions rule the transitions between the states. The propagation is briefly analyzed considering different initial conditions and network topologies (complete networks, random networks, scale-free networks and small-world networks), and interesting conclusions are derived.
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Acknowledgements
This research has been partially supported by Ministerio de Ciencia, Innovación y Universidades (MCIU, Spain), Agencia Estatal de Investigación (AEI, Spain), and Fondo Europeo de Desarrollo Regional (FEDER, UE) under project with reference TIN2017-84844-C2-2-R (MAGERAN) and the project with reference SA054G18 supported by Consejería de Educación (Junta de Castilla y León, Spain).
A. Bustos Tabernero thanks Ministerio de Educación y Formación Profesional (Spain) for his departmental collaboration grant in the Department of Applied Mathematics (University of Salamanca, Spain).
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Martín del Rey, A., Queiruga Dios, A., Hernández, G., Bustos Tabernero, A. (2020). Modeling the Spread of Malware on Complex Networks. In: Herrera-Viedma, E., Vale, Z., Nielsen, P., Martin Del Rey, A., Casado Vara , R. (eds) Distributed Computing and Artificial Intelligence, 16th International Conference, Special Sessions. DCAI 2019. Advances in Intelligent Systems and Computing, vol 1004. Springer, Cham. https://doi.org/10.1007/978-3-030-23946-6_12
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DOI: https://doi.org/10.1007/978-3-030-23946-6_12
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