Spatio-temporal malware containment model with alert

Alerting at the beginning of infection helps preventing malware from spreading and plays an important role before a definitive remedy is available. In this paper, we propose and study an epidemic model SASIR (susceptible-alert-susceptible-infected-recovered), based on stochastic cellular automata, which implements an alert strategy able to contain the spreading of malware. We show the advantage of the cellular automata to easily implement the containment algorithm, and perform simulations to evaluate the effectiveness of the strategy under some conditions. To assess the efficiency of the model from an economic point of view, we present a technique which compares the costs of the SASIR with those of the classical SIR model, and show that, in general, economic efficiency increases with the reduction in the number of infected, but diminishes with the ratio of the cost of alert to the cost of infected.