HinDom: A Robust Malicious Domain Detection System based on Heterogeneous Information Network with Transductive Classification
This work addresses the challenge of bypassing machine learning-based classifiers for cyber attackers, offering a novel approach to DNS security.
The authors tackled the problem of detecting malicious domains in DNS by proposing HinDom, a system that models DNS as a Heterogeneous Information Network and uses transductive classification, achieving accurate and robust detection with only a small fraction of labeled samples.
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.