A Bayesian optimization-based LSTM model for DGA domain name identification approach. Issue 1 (1st July 2022)

• Record Type: Journal Article
• Authors: Niu, Youfeng; Guan, Mingxi; Yuan, Wenhao; Chen, Yilin; Chen, Lingyi; Yu, Qiming
• Is Part Of: Journal of physics. Volume 2303:Issue 1(2022)
• Publication Date: 2022-07-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2303/1/012015
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In recent years, with the rapid development and rise of mobile Internet, network security issues have also posed a great threat to people. Botnets are an important problem faced by current network security. DNS protocol-based botnets widely use domain generation algorithm (DGA), which can randomly change the domain name to hide itself, and therefore is very likely to threaten people's network security. In this paper, we use the domain names of the top 1 million websites in the Alexa global ranking as white samples, and for the DGA sample data, we use the open data of 360netlab as black samples. The character sequence model is used for feature extraction, and the LSTM with Bayesian optimization neural network is used to optimize the hyperparameter combination, which finally makes the accuracy of the model above 97%, and the model has superior performance to compare with the conventional model, which can effectively improve the accuracy of DGA detection and recognition.