HinDom: A Robust Malicious Domain Detection System based on Heterogeneous Information Network with Transductive Classification


Xiaoqing Sun, Mingkai Tong, and Jiahai Yang, Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, China; Liu Xinran, National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China; Liu Heng, China Electronics Cyberspace Great Wall Co., Ltd, Beijing, China


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 more robust domain detection system named HinDom. Instead of relying on local features, HinDom obtains a global view by constructing a heterogeneous information network (HIN) 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 malicious domain detection. 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.

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@inproceedings {242062,
author = {Xiaoqing Sun and Mingkai Tong and Jiahai Yang and Liu Xinran and Liu Heng},
title = {{HinDom}: A Robust Malicious Domain Detection System based on Heterogeneous Information Network with Transductive Classification},
booktitle = {22nd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2019)},
year = {2019},
isbn = {978-1-939133-07-6},
address = {Chaoyang District, Beijing},
pages = {399--412},
url = {https://www.usenix.org/conference/raid2019/presentation/sun},
publisher = {USENIX Association},
month = sep