Open Access

ARTICLE

A Modified PointNet-Based DDoS Attack Classification and Segmentation in Blockchain

Jieren Cheng^1,3, Xiulai Li^1,2,3,4,*, Xinbing Xu^2,3, Xiangyan Tang^1,3, Victor S. Sheng⁵

1 School of Computer Science and Technology, Hainan University, Haikou, 570228, China
2 School of Cyberspace Security, Hainan University, Haikou, 570228, China
3 Hainan Blockchain Technology Engineering Research Center, Hainan University, Haikou, 570228, China
4 Hainan Hairui Zhong Chuang Technol. Co. Ltd., Haikou, 570228, China
5 Department of Computer Science, Texas Tech University, TX, 79409, USA

* Corresponding Author: Xiulai Li. Email:

Computer Systems Science and Engineering 2023, 47(1), 975-992. https://doi.org/10.32604/csse.2023.039280

Received 20 January 2023; Accepted 13 April 2023; Issue published 26 May 2023

Abstract

With the rapid development of blockchain technology, the number of distributed applications continues to increase, so ensuring the security of the network has become particularly important. However, due to its decentralized, decentralized nature, blockchain networks are vulnerable to distributed denial-of-service (DDoS) attacks, which can lead to service stops, causing serious economic losses and social impacts. The research questions in this paper mainly include two aspects: first, the classification of DDoS, which refers to detecting whether blockchain nodes are suffering DDoS attacks, that is, detecting the data of nodes in parallel; The second is the problem of DDoS segmentation, that is, multiple pieces of data that appear at the same time are determined which type of DDoS attack they belong to. In order to solve these problems, this paper proposes a modified PointNet (M-PointNet) for the classification and type segmentation of DDoS attacks. A dataset containing multiple DDoS attack types was constructed using the CIC-DDoS2019 dataset, and trained, validated, and tested accordingly. The results show that the proposed DDoS attack classification method has high performance and can be used for the actual blockchain security maintenance process. The accuracy rate of classification tasks reached 99.65%, and the accuracy of type segmentation tasks reached 85.47%. Therefore, the method proposed in this paper has high application value in detecting the classification and segmentation of DDoS attacks.

---

Keywords

---