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 based on only domain strings alone. Usually, such methods analyze the structure and semantic features of domain strings since simple AGDs show great difference in these two aspects. Among various types of AGDs, dictionary-based AGDs are unique for its semantic similarity to normal domains, which makes such detections 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 proposes Word-Map which is based on community detection algorithm to detect dictionary-based AGDs. Word-Map achieved an accuracy above 98.5% and recall rate above 99.0% on testing sets.
This work is supported by the National Key Research and Development Program of China (No.2017YFB0802300).
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Shen, Q., Zou, F. (2020). Detecting Dictionary Based AGDs Based on Community Detection. In: Park, N., Sun, K., Foresti, S., Butler, K., Saxena, N. (eds) Security and Privacy in Communication Networks. SecureComm 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 335. Springer, Cham. https://doi.org/10.1007/978-3-030-63086-7_3
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DOI: https://doi.org/10.1007/978-3-030-63086-7_3
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