Effects of dynamic quarantine and nonlinear infection rate in a model for computer worms propagation

We propose a new model for computer worms propagation, using dynamic quarantine and a nonlinear infection rate. The dynamic quarantine is based in epidemic disease control methods and in the principle ‘assume guilty before proven inocent’. This means that the host is blocked whenever its behavior looks suspicious. After a short time, the quarantined computer is released. The nonlinear infection rate is used to capture the dynamics of overcrowded infectious networks and high viral loads. We simulate numerically the model for distinct values of the quarantine times. We observe that increasing the quarantine time decreases the number of infectious hosts in the network.