SHDoS Attack Detection Research Based on Attention-GRU
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.
SHDoS attackBorderline-SMOTE oversampling algorithmself-attention mechanismgated recurrent unit
NETL
NSTL
万方数据
