Name
Title | ||
|---|---|---|
Hindom: A Robust Malicious Domain Detection System Based On Heterogeneous Information Network With Transductive Classification |
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2019 | PROCEEDINGS OF THE 22ND INTERNATIONAL SYMPOSIUM ON RESEARCH IN ATTACKS, INTRUSIONS AND DEFENSES | Transduction (machine learning),Computer science,Real-time computing,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoqing Sun | 1 | 3 | 1.75 |
| Mingkai Tong | 2 | 0 | 1.01 |
| Jiahai Yang | 3 | 200 | 53.58 |
| Xinran Liu | 4 | 28 | 13.23 |
| Heng Liu | 5 | 153 | 27.10 |