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

Authors: Rohidas Balu Sangore; Manoj Eknath Patil

Addresses: Department of Computer Engineering, SSBT's College of Engineering and Technology, Jalgaon, Maharashtra 425001, India ' Department of Computer Engineering, SSBT's College of Engineering and Technology, Jalgaon, Maharashtra 425001, India

Abstract: 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%).

Keywords: distributed denial of service attack; DDoS; internet of things; IoT; deep learning; DL; convolutional neural network; CNN.

DOI: 10.1504/IJICS.2025.148108

International Journal of Information and Computer Security, 2025 Vol.27 No.4, pp.437 - 464

Received: 01 Apr 2024
Accepted: 16 Oct 2024
Published online: 25 Aug 2025 *

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