  Internet worm attacks pose a significant threat to network security and
management. In this work, we coin the term Internet worm tomography as
inferring the characteristics of Internet worms from the observations of
Darknet or network telescopes that monitor a routable but unused IP address
space. Under the framework of Internet worm tomography, we attempt to infer
Internet worm temporal behaviors, i.e., the host infection time and the worm
infection sequence, and thus pinpoint patient zero or initially infected hosts.
Specifically, we introduce statistical estimation techniques and propose method
of moments, maximum likelihood, and linear regression estimators. We show
analytically and empirically that our proposed estimators can better infer worm
temporal characteristics than a naive estimator that has been used in the
previous work. We also demonstrate that our estimators can be applied to worms
using different scanning strategies such as random scanning and localized
scanning.
