There has been a constant barrage of worms over the Internet during the recent past. Besides threatening network security, these worms cause an enormous economic burden in terms of loss of productivity at the victim hosts. In addition, these worms create unnecessary network data traffic that causes network congestion, thereby hurting all users. To develop appropriate tools for thwarting quick spread of worms, researchers are trying to understand the behavior of the worm propagation with the aid of epidemiological models. In this study, we apply the classical SIS model and a modification of SIR model to simulate worm propagation in two different network topologies. Whereas in the SIR model once a node is cured after infection it becomes permanently immune, our modification allows this immunity to be temporary, since the cured nodes may again become infected, maybe with a different strain of the same worm. The simulation study also shows that time to infect a large portion of the network vary significantly depending on where the infection begins. This information could be usefully employed to choose hosts for quarantine to delay worm propagation to the rest of the network
['Jonghyun Kim', 'Sridhar Radhakrishnan', 'Sudarshan K. Dhall']
Measurement and analysis of worm propagation on Internet network topology