《山东大学学报(理学版)》 ›› 2019, Vol. 54 ›› Issue (7): 106-112.doi: 10.6040/j.issn.1671-9352.0.2019.249
• • 上一篇
刘洋1,赵科军1,2*,葛连升1,刘恒3
LIU Yang1, ZHAO Ke-jun1,2*, GE Lian-sheng1, LIU Heng3
摘要: 提出了一种基于深度学习的CNN-LSTM-Concat快速DGA域名分类算法,使用多层一维卷积网络对域名字符进行序列化处理,LSTM网络层用于强化获取字符间长距离依赖关系。通过将LSTM的多序列输入转化为单向量输入,在保证检测性能的前提下,能够大幅提高训练和检测速度。实验证明,我们的方法对DGA域名分类的准率在公开数据集上达到98.32%。同时,在准确率相比主流的LSTM方法更高的情况下,检测时间比LSTM方法快6.41倍。
中图分类号:
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