DDoS attack detection in blockchain network layer using dual attention based dense convolutional gated recurrent unit

This paper aims to design a novel hybrid deep learning model along with a new feature extraction technique. This paper collects the input data from publicly available datasets and is pre-processed by using min-max normalisation and missing value imputation to eliminate unnecessary information. After, a new squeeze excited deep ResNet-152 (SE-DRes152) model is introduced to extract the essential traffic attributes from pre-processed data. Finally, the DDoS attack from the provided inputs is identified by presenting a novel dual attention-based dense convolutional gated recurrent unit (DA_DCGRU) approach based on the extracted features. The ability of the proposed classifier is further enhanced by fine-tuning its parameters using by modified fire hawks (MFH) approach. The simulation results and comparison analysis prove that the proposed model outperforms the other existing methods in terms of accuracy (98.83%), precision (97.54%), recall (97.81%), F-score (97.67%), specificity (98.51%), MAE (0.192%), MSE (0.01%) and RMSE (0.1082%).