Modeling the Spread of Malware on Complex Networks

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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