  Domain name system (DNS) is a crucial part of the Internet, yet has been
widely exploited by cyber attackers. Apart from making static methods like
blacklists or sinkholes infeasible, some weasel attackers can even bypass
detection systems with machine learning based classifiers. As a solution to
this problem, we propose a robust domain detection system named HinDom. Instead
of relying on manually selected features, HinDom models the DNS scene as a
Heterogeneous Information Network (HIN) consist of clients, domains, IP
addresses and their diverse relationships. Besides, the metapath-based
transductive classification method enables HinDom to detect malicious domains
with only a small fraction of labeled samples. So far as we know, this is the
first work to apply HIN in DNS analysis. We build a prototype of HinDom and
evaluate it in CERNET2 and TUNET. The results reveal that HinDom is accurate,
robust and can identify previously unknown malicious domains.
