Authors:
Shaojie Chen
1
;
Bo Lang
1
;
2
and
Chong Xie
1
Affiliations:
1
State Key Laboratory of Software Development Environment, Beihang University, Beijing, China
;
2
Zhongguancun Laboratory, Beijing, China
Keyword(s):
Fast-Flux Domain Name Detection, Domain Resolution Spatial Features, Resolution Spatial Relationship Graph, GCN, Botnet.
Abstract:
Fast-Flux malicious domain names evade detection by quickly changing the resolved IP addresses of the domain name, and play an important role in cyberattacks. In order to improve the performance of the Fast-Flux domain name detection, this paper explores and uses the rich spatial features contained in the domain name resolution process, and proposes a Fast-Flux malicious domain name detection method based on the domain resolution spatial features. In this method, the CNAMEs and IPs in the resolution results obtained by multiple requests are used as nodes to construct the resolution spatial relationship graph (RSRG). Then the NS record of the second-level domain name, Geographical locations and Autonomous System Numbers of the resolved IPs, and WHOIS information of the domain name are further extracted as the node features in the RSRG. Finally, a GCN model with Max Pooling algorithm is used to extract spatial features from RSRG and perform classification. Our method achieves an accura
cy of 94.98% and an F1 value of 92.02% on the self-constructed dataset, and the overall performance is significantly better than the current best methods.
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