Research on malicious domain name detection method based on deep learning

被引:0
作者
Ren, Fei [1 ]
Jiao, Di [1 ]
机构
[1] State Informat Ctr, Beijing 100045, Peoples R China
来源
PROCEEDINGS OF 2024 3RD INTERNATIONAL CONFERENCE ON CRYPTOGRAPHY, NETWORK SECURITY AND COMMUNICATION TECHNOLOGY, CNSCT 2024 | 2024年
关键词
malicious domain name detection; Deep learning; BERT model: TextCNN model; Attention mechanism;
D O I
10.1145/3673277.3673292
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study aims to address complex network attacks by proposing a malicious domain detection model based on a combination of BERT-TextCNN with attention mechanisms. The BERT model is employed to learn contextual semantics and generate rich semantic representations, while TextCNN contributes local feature extraction capabilities. The integration of global and local attention mechanisms allows targeted focus on key information in URLs, enhancing adaptability to various attack methods. Experimental results across multiple datasets demonstrate the model's outstanding performance in accuracy, precision, recall, and F1-Score, achieving an accuracy rate of 96.67%. In comparison to traditional methods, the proposed model maintains high detection accuracy while exhibiting a broader detection range, providing a reliable deep learning solution for malicious domain detection.
引用
收藏
页码:81 / 85
页数:5
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