24-03-2012, 04:42 PM
Epidemic models applied to worms on Internet
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INTRODUCTION
Internet worms, self-propagating over the Internet, causing
significant damages to the Internet infrastructure, and
exploiting vulnerabilities in popular operating systems and
applications, have addressed a serious threat to confidentiality,
integrity, and availability of computer resources on the
Internet. How to combat worms effectively is an urgent issue
confronted by defenders.
THE WORM CONTAINMENT MODEL BASED ON DYNAMIC QUARANTINE
The total population N is partitioned into five groups, and
any host can potential be in any of these groups at any time
tick t: Susceptible-all hosts in this group are vulnerable to
worm infection, and can acquire the worm infection when
in contact with an infected host; Exposed-all hosts in this
group are exposed to the infection but do not exhibit any
outward symptoms; Infected-all hosts in this group carry and
propagate the infection; Quarantined-all hosts in this group
have exhibited suspicious behavior and consequently, have
been quarantined; Vaccinated-all hosts in this group have
been vaccinated and are immune to the worm infection.
IV. PERFORMANCE EVALUATION
We choose the Slammer worm as basic behavior of a
worm in this experiment. We assume that the total vulnerable
population is N = 75, 000. Slammer is a bandwidth-limited
worm with an average scan rate s = 4000 scans/second
[11]. The size of Slammer worm is 404 bytes [11]. We
also assume I(0) = 10. When considering the dynamic
quarantine, we assume that the time before a host is alarmed
follows exponential distribution: the quarantine rate of infected
hosts is λ1 = 0.2 per second; the quarantine rates
of susceptible and exposed hosts are λ3 = 0.00002315 per
second and λ2 = 0.00002315 per second, respectively. Let
the quarantine time to be T = 10 seconds.
CONCLUSIONS
This paper proposed the SEIQV model based on epidemiological
studies and deduced the conditions for the
asymptotically stability of the worm-free equilibrium P0.