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People use the Internet to shop, access information and enjoy
entertainment by browsing web sites. At the same time, cyber-criminals
operate malicious domains to spread illegal information and acquire
money, which poses a great risk to the security of cyberspace. There-
fore, it is of great importance to detect malicious domains in the eld
of cyberspace security. Typically, there are broad research focusing on
detecting malicious domains either by blacklist or exploiting the features
via machine learning techniques. However, the former is infeasible due
to the limited crowd, and the later requires complex feature engineering.
Di erent from most of previous methods, in this paper, we propose a
novel lightweight solution named DomainObserver to detect malicious
domains. Our technique of DomainObserver is based on dynamic time
warping that is used to better align the time series. To the best of our
knowledge, it is a new trial to apply passive trac measurements and
time series data mining to malicious domain detection. Extensive exper-
iments on real datasets are performed to demonstrate the e ectiveness
of our proposed method.
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