Name
Abstract | ||
|---|---|---|
Domain generation algorithms (DGA) are widely used by malware families to realize remote control. Researchers have tried to adopt deep learning methods to detect algorithmically generated domains (AGD) automatically. Some detection methods based on only domain strings alone are proposed. Usually, such methods analyze the structure and semantic features of domain strings. Among various types of AGDs, dictionary-based AGDs are unique for their semantic similarity to normal domains, which makes such detection based on only domain strings difficult. In this paper, we observe that the relationship between domains generated based on a same dictionary shows graphical features. We focus on the detection of dictionary-based AGDs and propose Word-Map which is based on community detection algorithm to detect dictionary-based AGDs. Word-map achieved great accuracy, recall rate, false positive rate, and missing rate on testing sets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-91356-4_21 | INFORMATION SECURITY (ISC 2021) |
Keywords | DocType | Volume |
Algorithmically generated domains, Community detection, Machine learning | Conference | 13118 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Futai Zou | 1 | 8 | 3.16 |
| Qianying Shen | 2 | 0 | 0.34 |
| Yuzong Hu | 3 | 0 | 0.34 |