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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 10

DLC-TSM：multi-dimensional feature image approach for crypto-malicious traffic classification

Guo Yijie1
Luo Qin1
Wu Peng2
1. School of Computer science & software engineering, Southwest Petroleum University, Chengdu 610500, China
2. School of Information & Engineering, Sichuan Tourism University, Chengdu 610500, China
DOI: 10.19734/j.issn.1001-3695.2024.12.0535

Abstract

To address the limitations of existing encrypted malicious traffic classification methods, such as restricted feature representation capability, high noise sensitivity, and insufficient model generalization, this study proposes an encrypted malicious traffic classification method based on deep learning using three-dimensional second-order Markov matrix images (DLC-TSM) . By integrating the feature transformation of three-dimensional second-order Markov probability matrices with deep learning, the original network traffic is converted into red-green-blue RGB feature images. An innovative threshold filtering algorithm is applied to enhance feature representation, and deep learning neural networks are employed to extract deep image features for precise traffic classification. Experimental results demonstrate the effectiveness of the proposed method across four encrypted malicious traffic datasets in three different scenarios, achieving peak accuracies of 99.33%, 99.07%, 97.98%, and 98.17%, respectively. DLC-TSM effectively addresses the trade-off between encrypted traffic feature extraction and classification accuracy, and its image-based representation strategy offers a novel technical approach for traffic analysis research.

Foundation Support

国家自然科学基金资助项目(U2133208)
四川省科技计划重点研发项目(2022YFG0323)
成都市科技计划重点研发项目(2022-YF05-00451-SN)
西南石油大学研究生教改项目(2024JGZD018)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.12.0535
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 10

Publish History

[2025-06-04] Accepted Paper

Cite This Article

郭逸杰, 罗琴, 吴鹏. DLC-TSM：一种用以加密恶意流量分类的高维特征图方法 [J]. 计算机应用研究, 2025, 42 (10). (2025-06-04). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0535. (Guo Yijie, Luo Qin, Wu Peng. DLC-TSM：multi-dimensional feature image approach for crypto-malicious traffic classification [J]. Application Research of Computers, 2025, 42 (10). (2025-06-04). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0535. )

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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