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Markov Random Fields for Malware Propagation: The Case of Chain Networks

Epidemic and stochastic models have been employed for describing the dynamic behavior of malware outbreaks. However, most of them lack a holistic treatment of the problem. In this work, we model malware propagation as a Markov Random Field and employ Gibbs sampling for the analysis of the system. We demonstrate the proposed framework for the case of a chain network, a model often emerging in both wired and wireless multi-hop networks.