Statistical Inference of Computer Virus Propagation Using Non-Homogeneous Poisson Processes

This paper presents statistical inference of computer virus propagation using non-homogeneous Poisson processes (NHPPs). Under some mathematical assumptions, the number of infected hosts can be modeled by an NHPP. In particular, this paper applies a framework of mixed-type NHPPs to the statistical inference of periodic virus propagation. The mixed-type NHPP is defined by a superposition of NHPPs. In numerical experiments, we examine a goodnessof-fit criterion of NHPPs on fitting to real virus infection data, and discuss the effectiveness of the model-based prediction approach for computer virus propagation.