In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Technology of Information Security
|
App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 3
898-904

Malware detection based on genetic programming and ensemble learning

Qiao Mengqing1a,1b
Li Lin1a,1b
Wang Jie2
Wan Zhenhua2
1. a. School of Computer Science & Technology, b. Hubei Key Laboratory of Intelligent Information Processing & Real-time Industrial Systems, Wuhan University of Science & Technology, Wuhan 430065, China
2. Shenzhen Open Source Internet Security Technology Co. , Ltd. , Shenzhen Guangdong 518000, China
DOI: 10.19734/j.issn.1001-3695.2022.05.0376

Abstract

In recent years, malware is constantly developing and changing, resulting in a low accuracy of a single detection model. Using ensemble learning to combine multiple models can improve the detection effect, but the accuracy and diversity of base learners in the ensemble model is difficult to balance. Therefore, this paper proposed an ensemble model generation method based on genetic programming to generate the optimal ensemble model for malware detection. Genetic programming could integrate feature processing and ensemble model construction into a single GP tree, which solved the problem that traditional malware ensemble detection models was difficult to balance individual accuracy and diversity. The method used the detection rate of malware in the ensemble model as the basis of population evolution to ensure the accuracy of the base learner. When generated an ensemble model, it could automatically select the feature processing method, classification algorithm and optimize the hyperparameters of the base classifier, and ensured the diversity of the base learner through the perturbation of input attributes and algorithm parameters. According to the idea of survival of the fittest, it evolved to generate the optimal ensemble model with high accuracy and diversity. The experimental results on the EMBER dataset show that the detection accuracy of the best ensemble model reaches 98.88%. Further analyses show that the model has high diversity and interpretability.

Foundation Support

武汉市重点研发计划资助项目(2022012202015070)
武汉科技大学研究生教改研究项目(Yjg202111)
湖北省教育厅资助项目(2020354)
湖北省大学生创新创业训练计划项目(S202110488047)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.05.0376
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 3
Section: Technology of Information Security
Pages: 898-904
Serial Number: 1001-3695(2023)03-042-0898-07

Publish History

[2022-10-20] Accepted Paper
[2023-03-05] Printed Article

Cite This Article

乔梦晴, 李琳, 王颉, 等. 基于遗传规划和集成学习的恶意软件检测 [J]. 计算机应用研究, 2023, 40 (3): 898-904. (Qiao Mengqing, Li Lin, Wang Jie, et al. Malware detection based on genetic programming and ensemble learning [J]. Application Research of Computers, 2023, 40 (3): 898-904. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)