Network virus propagation is influenced by various factors, and some of them are neglected in most of the existed models. So, mathematical model of network virus propagation is simplified. In fact, many factors are very important during the virus propagation. In this paper, we investigate epidemiological models to reason about email virus propagation. The paper extended the classical virus propagation model SEIR for incorporating two new parameters: User Vigilance and Removing Time. We show that these parameters greatly influence the virus propagation. The fruitful simulations will demonstrate that this developed model can be used for describing email virus propagation and calculating the costs of virus outbreak. We also prove that the time of anti-virus technique appearing plays an important role in controlling virus propagation.

In this paper, we simulate the Code Red II and Nimda worms on different enterprise-scale networks to determine the impact that topology has on worm propagation. A corporate network can be designed to improve security and, as we show, to decrease the propagation rate of worms that use network scanning as a target discovery technique. We also examine the impact that LaBrealike devices have on propagation rates and compare it to the impact of network topology.

We study the fundamental problem of how devices, operating over a wireless medium, can discover potential communication partners. For the devices, the spectrum is divided into m frequency channels. As multiple devices share these channels, they have to cope with collisions while they are trying to find other devices in the system. Moreover, we assume an adversarial jammer to block some of the available channels. The devices cannot distinguish between unvoluntary collisions, jamming, and empty channels, i.e. channels on which no device is currently transmitting its information. We construct algorithms efficiently solving the described discovery problem even if the number of jammed channels is unknown to the devices. Moreover, we discuss several jammer models and compare our algorithms to the inquiry