DGA domain name detection model based on multiscale feature

Huajie Luo; Wanping Liu; Qiong Cao

doi:10.1117/12.2683451

26 June 2023 DGA domain name detection model based on multiscale feature

Huajie Luo, Wanping Liu, Qiong Cao

Proceedings Volume 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023); 1272109 (2023) https://doi.org/10.1117/12.2683451
Event: 2023 2nd International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2023, Chengdu, China

Abstract

Massive botnet attacks pose a serious threat to social stability and network security. To avoid security interception, botnets mainly use Domain Generation Algorithm (DGA) to dynamically generate a large number of malicious domain names to establish communication. Therefore, it is important to study how to detect DGA domain names more effectively, and this paper proposes a method to detect DGA domain names based on multi-scale features. In the domain name feature extraction phase, extracting domain name combination features on a multi-scale convolutional neural network (CNN) based on a compressed activation model. Simultaneously combined with bi-directional gated recurrent unit (BiGRU) to extract domain name sequence features and build hybrid deep learning models to achieve the detection of DGA domain names based on lexical combination generation. The experimental results show that the method improves the F-Score evaluation metric by 7.25% in the binary classification task compared to the CNN-only model, and also has higher detection precision for lexicon-based domain names like suppobox.

Citation Download Citation

Huajie Luo, Wanping Liu, and Qiong Cao "DGA domain name detection model based on multiscale feature", Proc. SPIE 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 1272109 (26 June 2023); https://doi.org/10.1117/12.2683451

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KEYWORDS

Feature extraction

Machine learning

Performance modeling

Network security

Convolution

Deep learning

Convolutional neural networks

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