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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 2

DDoS attack detection model based on Takens-Transformer and GCN

Deng Yuyang
Lu Tianliang
Li Zhihao
Meng Haoyang
Li Jinru
School of Information Network Security, People's Public Security University of China, Beijing 100038, China
DOI: 10.19734/j.issn.1001-3695.2025.05.0218

Abstract

To address the problems of weak adaptability and high computational cost in existing distributed denial of service (DDoS) attack detection, this study proposed a Transformer model based on time-delay embedding and graph convolutional network (TDE-TGCN) . The method utilized Takens theorem to model network traffic as a dynamical system and revealed the impact of DDoS attacks on nonlinear traffic features through time-delay embedding. It employed Transformer model to map traffic sequences to high-dimensional space and captured burstiness and global correlations through multi-head attention mechanism. The approach combined graph convolutional network to mine topological information and cross-node attack patterns. On CIC-IDS2017 and other datasets as well as unknown attack scenarios simulated by feature variation, TDE-TGCN achieved detection accuracy of 98.7%, reduced false positive rate to 1.2%, and improved computational efficiency by 35%. Ablation experiments verified the significant contribution of each component to model performance. This research reexamined network traffic features from a dynamical system perspective and proposed a detection framework combining theory and practice, which provides an effective technical solution for DDoS attack detection in complex network environments.

Foundation Support

公安部科技计划项目(2023JSM09)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.05.0218
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 2

Publish History

[2025-10-27] Accepted Paper

Cite This Article

邓钰洋, 芦天亮, 李知皓, 等. 基于Takens-Transformer与GCN的DDoS攻击检测 [J]. 计算机应用研究, 2026, 43 (2). (2025-11-04). https://doi.org/10.19734/j.issn.1001-3695.2025.05.0218. (Deng Yuyang, Lu Tianliang, Li Zhihao, et al. DDoS attack detection model based on Takens-Transformer and GCN [J]. Application Research of Computers, 2026, 43 (2). (2025-11-04). https://doi.org/10.19734/j.issn.1001-3695.2025.05.0218. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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