Netinfo Security ›› 2023, Vol. 23 ›› Issue (10): 8-15.doi: 10.3969/j.issn.1671-1122.2023.10.002

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Research on DGA Malicious Domain Name Detection Method Based on Transfer Learning and Threat Intelligence

YE Huanrong1,2, LI Muyuan1,3, JIANG Bo4,5()   

  1. 1. School of Information Network Security, People’s Public Security University of China, Beijing 100038, China
    2. Cyber Police Division of Zigong Municipal Public Security Bureau, Zigong 643000, China
    3. Cyber Police Division of Qingdao Municipal Public Security Bureau, Qingdao 266000, China
    4. Institute of Information Engineering, Chinese Academy of Sciences, Beijing 10085, China
    5. School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2023-05-09 Online:2023-10-10 Published:2023-10-11

Abstract:

Domain name generation algorithms have been widely used in various types of cyber attacks, which have the characteristics of rapid sample change, many variants, and difficult to obtain, leading to low detection accuracy and poor warning capability of existing traditional models. To address this situation, a DGA malicious domain detection method based on transfer learning and threat intelligence was proposed, which extracted malicious domain context and semantic relationship features by building a combined model of bidirectional long short-term memory neural network and Transformer, pre-trains by using a publicly available large-sample malicious domain dataset, and transfered the training parameters to a new unknown small-sample malicious domain of APT organizations held by threat intelligence for model detection performance testing. The experimental results show that the model can achieve an average detection accuracy of 96.14% in a small-sample dataset of malicious domains used by APT organizations, and the detection performance is good.

Key words: malicious domain name, transfer learning, threat intelligence, Bi-LSTM, Transformer

CLC Number: