基于Attention-GRU的SHDoS攻击检测研究
SHDoS Attack Detection Research Based on Attention-GRU
江魁 1卢橹帆 2苏耀阳 2聂伟2
作者信息
- 1. 深圳大学信息中心,深圳 518060
- 2. 深圳大学电子与信息工程学院,深圳 518060
- 折叠
摘要
针对SHDoS发起变频攻击导致阈值检测方案失效的问题,文章提出一种基于Attention-GRU的深度学习模型.该模型首先利用改进的Borderline-SMOTE进行数据平衡处理,然后引入自注意力机制构建双层GRU分类网络,对预处理后的数据进行学习训练,最后对SHDoS攻击流量进行检测.在CICIDS2018数据集和SHDoS自制数据集上进行验证,实验结果表明,文章所提模型的精确率分别为98.73％和97.64％,召回率分别为96.57％和96.27％,相较于未采用自注意力机制的模型,在精确率和召回率上有显著提升,相较于以往采用SMOTE或Borderline-SMOTE进行数据预处理的模型,文章所提模型的性能也是最佳的.
Abstract
Aiming at the problem that SHDoS initiates a frequency conversion attack that causes the threshold detection scheme to fail,a deep learning model based on attention-GRU was proposed.The model used the improved Borderline-SMOTE for data balance processing firstly,then introduced the self-attention mechanism to build a two-layer GRU classification network,learned and trained the preprocessed data,and analyzed the SHDoS attack traffic to test finally.Verified by the CICIDS2018 dataset and self-built ShDoS dataset,and the experimental results shows that the accuracy rate of the model is 98.73％and 97.64％respectively,the recall rate is 96.57％and 96.27％respectively.The model with self-attention mechanism shows significant improvement compared to the model without it,compared to other models that use SMOTE or Borderline-SMOTE for data preprocessing,the performance of this model is also the best.
关键词
SHDoS攻击/Borderline-SMOTE过采样算法/自注意力机制/门控循环单元Key words
SHDoS attack/Borderline-SMOTE oversampling algorithm/self-attention mechanism/gated recurrent unit引用本文复制引用
基金项目
教育部未来网络创新研究与应用项目(2021FNB01001)
出版年
2024
NETL
NSTL
万方数据