注意差距：通过域随机化和元强化学习实现可泛化的自动化渗透测试

周仕承; 刘京菊; 陆余良; 杨家海; 张悦; 陈杰

doi:10.1631/FITEE.2500100

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注意差距：通过域随机化和元强化学习实现可泛化的自动化渗透测试

常规文章 | Updated：2026-01-12

- 注意差距：通过域随机化和元强化学习实现可泛化的自动化渗透测试
  Enhanced Publication
- Mind the Gap: towards generalizable autonomous penetration testing via domain randomization and meta-reinforcement learning
- “在自主渗透测试领域，研究人员取得了重大进展。他们建立了GAP框架，为解决训练环境困境和泛化能力差的问题提供了解决方案，为自主渗透测试研究开辟了新的方向。”
- Frontiers of Information Technology & Electronic Engineering 2025年26卷第12期页码：2511-2528
- Affiliations：
  
  1.College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
  2.Anhui Province Key Laboratory of Cyberspace Security Situation, Awareness and Evaluation, Hefei 230037, China
  3.Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing 100084, China
  4.College of Computer Science and Technology, National University of Defense Technology, Changsha 410073, China
- Author bio：
  
  E-mail: zhoushicheng@nudt.edu.cn
  ‡ Corresponding authors
- Funds：
- DOI：10.1631/FITEE.2500100
  中图分类号： TP393
- 收稿：2025-02-16，
  
  修回：2025-10-17，
  
  网络出版：2025-12-12，
  
  纸质出版：2025-12
- Accepted：
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周仕承, 刘京菊, 陆余良, 等. 注意差距：通过域随机化和元强化学习实现可泛化的自动化渗透测试[J]. Frontiers of Information Technology & Electronic Engineering, 2025,26(12):2511-2528.

Shicheng ZHOU, Jingju LIU, Yuliang LU, et al. Mind the Gap: towards generalizable autonomous penetration testing via domain randomization and meta-reinforcement learning[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(12): 2511-2528.
周仕承, 刘京菊, 陆余良, 等. 注意差距：通过域随机化和元强化学习实现可泛化的自动化渗透测试[J]. Frontiers of Information Technology & Electronic Engineering, 2025,26(12):2511-2528. DOI： 10.1631/FITEE.2500100.

Shicheng ZHOU, Jingju LIU, Yuliang LU, et al. Mind the Gap: towards generalizable autonomous penetration testing via domain randomization and meta-reinforcement learning[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(12): 2511-2528. DOI： 10.1631/FITEE.2500100.

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注意差距：通过域随机化和元强化学习实现可泛化的自动化渗透测试

Mind the Gap: towards generalizable autonomous penetration testing via domain randomization and meta-reinforcement learning

DOI：10.1631/FITEE.2500100