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Technology of Information Security
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 2
582-591

Malicious URL detection framework based on EMO-GAN

Geng Haijun1a,2
Wei Chao1a
Hu Zhiguo1b
Guo Xiaoying1a
Chi Haotian1a
Yang Jing1a
1. a. School of Automation & Software Engineering, b. School of Computer & Information Technology, Shanxi University, Taiyuan 030006, China
2. Shanxi Qingzhong Technology Co. , Ltd. , Taiyuan 030000, China
DOI: 10.19734/j.issn.1001-3695.2024.04.0212

Abstract

With the widespread application of the World Wide Web and the increasing severity of network threats, the security of URL has become a research focus in the field of network security. How to effectively detect and prevent malicious URLs has become a significant concern in the industry. In response to the challenges of data acquisition, insufficient feature representation, and model concept drift faced by malicious URL detection, this paper proposed a malicious URL detection framework based on EMO-GAN(EMO-GANUDF). This framework effectively addressed the issue of data acquisition difficulties by combining extremely randomized trees(ET) and margin generative adversarial networks(MarginGAN) for semi-supervised learning. In terms of feature extraction, the framework proposed a feature representation method that integrated statistical, character, and lexical features, achieving efficient feature representation of URLs. Additionally, to address the problem of model concept drift, the framework introduced a classifier that supported online learning, enhancing the scalability and adaptability of the model. Through comparative experiments with different detection methods on multiple datasets, the proposed method achieves 99% accuracy and 84% F1 score on the malicious URLs public dataset, outperforming other detection methods and proving its effectiveness and superiority.

Foundation Support

国家自然科学基金资助项目(62472267)
山西省应用基础研究计划资助项目(20210302123444,20210302123455)
中国高校产学研创新基金资助项目(2021FNA02009)
国家自然科学基金资助项目(61702315,61906115,62472267)
同济大学嵌入式系统与服务计算教育部重点实验室开放课题(ESSCKF2021-04)
山西省重点研发计划资助项目(201903D421003)
国家重点研发计划资助项目(2018YFB1800401)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.04.0212
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 2
Section: Technology of Information Security
Pages: 582-591
Serial Number: 1001-3695(2025)02-036-0582-10

Publish History

[2025-02-05] Printed Article

Cite This Article

耿海军, 蔚超, 胡治国, 等. 基于EMO-GAN的恶意URL检测框架 [J]. 计算机应用研究, 2025, 42 (2): 582-591. (Geng Haijun, Wei Chao, Hu Zhiguo, et al. Malicious URL detection framework based on EMO-GAN [J]. Application Research of Computers, 2025, 42 (2): 582-591. )

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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