计算机应用与软件2024，Vol.41Issue(6)：336-341,6.DOI:10.3969/j.issn.1000-386x.2024.06.048
基于CNN-BiGRU的恶意域名检测方法
MALICIOUS DOMAIN DETECTION METHOD BASED ON CNN-BIGRU
摘要
Abstract
Malicious domain name detection is of great significance to prevent botnet and other network attacks.This paper proposes a malicious domain name detection method called CNN-BiGRU-Focal.Convolutional neural network and bidirectional gated cyclic unit network were used for feature fusion learning,and an improved focal loss function was introduced to solve the problem of data imbalance.Compared with LSTM,CNN,GRU and ATT-CNN-BiLSTM method,the detection accuracy of the proposed method is improved by 1.43,2.89,1.27 and 2.43 percentage points in multi-classification experiments,and 0.19,0.12,1.41 and 0.3 percentage points in binary classification experiments.Experiments show that CNN-BiGRU-Focal method has better performance in the detection of malicious domain names.关键词
域名生成算法/深度学习/卷积神经网络/双向门控循环单元网络Key words
DGA/Deep learning/CNN/BiGRU分类
信息技术与安全科学引用本文复制引用
林梓宇,凌捷..基于CNN-BiGRU的恶意域名检测方法[J].计算机应用与软件,2024,41(6):336-341,6.基金项目
广东省重点领域研发计划项目(2019B010139002) （2019B010139002）
广州市重点领域研发计划项目(202007010004). （202007010004）
