A DDoS-Attack Detection Method Oriented to the Blockchain Network Layer. (4th May 2022)

• Record Type: Journal Article
• Authors: Dai, Qian-yi; Zhang, Bin; Dong, Shu-qin
• Other Names: Fu AnMin Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-05-04
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/5692820
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : By nature, a traditional attack method, denial-of-service (DDoS) attack poses a considerable threat to the security of the blockchain network layer. This paper proposes a distributed DDoS-attack traffic detection method based on a cross multilayer convolutional neural network model in the blockchain network layer. The method resolves the low generalisation, high misreporting rate, and low detection efficiency problems of the existing detection methods, which are caused by nondistinctive core features and the high complexity of robust features when detecting DDoS attacks transmitted by mixed protocols on a blockchain network layer. First, the model performs a convolution operation on preprocessed traffic on the blockchain network layer using a cross-layer method based on L 2 regularisation. After this operation, the model can perceive the detailed features of attack traffic from multiple levels while enhancing the representational performance of key features; specifically, the parameters with high-variance terms are penalised to limit changes in the model's weight parameters. The highly robust abstract features of attack traffic are extracted, thereby increasing the generalisation ability and reducing the misreporting rate of the model. Second, parametric encoding of the abstract features is performed by a stacked sparse autoencoder based on Kullback–Leibler divergence, and the sparsity of the model is adjusted to reduce the redundant data and the coupling between abstract features. The outputs of the encoded features are then effectively categorised. Finally, the global optimisation of parameters is performed by an improved random gradient-descent algorithm, which prevents oscillation of the training parameters and accelerates the model convergence. In an experimental evaluation, the proposed method achieved satisfactory binary- and multiclass detection of DDoS-attack traffic on both CSE-CIC-IDS 2018 on the AWS dataset and on the real mixed data of a blockchain network layer.