App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 8

Adversarial sample generation and robustness evaluation method for botnet intrusion detection systems in industrial control systems

Lu Jiazhong1a,2,3
Liu Junrui1b,2,3
Peng Jimin1b,2,3
Zhang Chong1a,2,3
Shu Jian1b,2,3
Liu Xiaolei4
1. a. School of Artificial Intelligence, b. School of Cybersecurity (Xin Gu Industrial College), Chengdu University of Information Technology, Chengdu 610225, China
2. Sichuan Provincial Key Laboratory of Advanced Cryptography Technology and System Security, Chengdu 610054, China
3. SUGON Industrial Control and Security Center, Chengdu 610225, China
4. National Engineering Physics Interdisciplinary Science Research Center, Mian yang 621000, China
DOI: 10.19734/j.issn.1001-3695.2025.11.0492

Abstract

Robustness of botnet intrusion detection models in industrial control systems (ICS) under adversarial perturbations remains insufficiently understood. A systematic study and experimental evaluation assessed this vulnerability in ICS botnet detection. An adversarial sample generation method for ICS botnet scenarios combined time-domain and frequency-domain noise to construct representative perturbed traffic samples that emulate complex adversarial environments. Traffic features integrated statistical flow attributes with industrial protocol identifiers, and a botnet intrusion dataset was collected from a real physical environment. Adversarial robustness was evaluated on eight mainstream intrusion detection models under three real ICS attack scenarios. Experimental results show that the generated adversarial samples significantly reduce detection accuracy and recall across multiple models. The findings reveal inherent robustness deficiencies of existing detection models in adversarial conditions and provide reproducible data samples and quantitative evaluation evidence to support adversarial training and defense mechanism design.

Foundation Support

四川省自然科学基金资助项目(2025ZNSFSC0507)
国家自然科学基金资助项目(62102049)
先进密码技术与系统安全四川省重点实验室开放基金资助项目(SKLACS-202402)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.11.0492
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 8

Publish History

[2026-04-23] Accepted Paper

Cite This Article

卢嘉中, 刘君芮, 彭基珉, 等. 工控系统中僵尸网络入侵检测系统的对抗样本生成方法与鲁棒性评估 [J]. 计算机应用研究, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0492. (Lu Jiazhong, Liu Junrui, Peng Jimin, et al. Adversarial sample generation and robustness evaluation method for botnet intrusion detection systems in industrial control systems [J]. Application Research of Computers, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0492. )

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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