基于Attention-GRU的SHDoS攻击检测研究

SHDoS Attack Detection Research Based on Attention-GRU

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SHDo S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。

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.