人工智能赋能网络攻防平台的路径与进展

王群; 李馥娟; 高光亮

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人工智能赋能网络攻防平台的路径与进展

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- 人工智能赋能网络攻防平台的路径与进展
- The Path and Progress of Empowering Network Attack and Defense Platforms with Artificial Intelligence
- 电信科学 2026年
- 作者机构：
  
  1.江苏警官学院计算机信息与网络安全系，江苏南京 210031
  2.江苏省电子数据取证分析工程研究中心，江苏南京 210031
- 作者简介：
  
  [ "王群（1971-）, 男,甘肃天水人，博士, 教授，硕士生导师，CCF杰出会员(33021D)，主要研究领域为信息安全，计算机网络体系结构与协议。" ]
  [ "李馥娟（1974-），女，陕西西安人，硕士，教授，硕士生导师，主要研究领域为计算机网络技术与应用，信息安全。" ]
  [ "高光亮（1989-），男，山东临朐人，博士，副教授，硕士生导师，江苏高校“青蓝工程”优秀青年骨干教师培养对象。主要研究领域为公安大数据挖掘，社会网络安全等方向。" ]
- 基金信息：
  
  国家自然科学基金(72401110);江苏省高校优秀科技创新团队;公安技术、网络空间安全“十四五”江苏省重点学科;教育部人文社会科学研究规划基金(24YJAZH158)
- DOI：
  中图分类号： TP391
- 收稿：2026-01-04，
  
  修回：2026-03-02，
  
  录用：2026-03-02，
- 稿件说明：
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王群, 李馥娟, 高光亮. 人工智能赋能网络攻防平台的路径与进展[J/OL]. 电信科学, 2026.

WANG Qun, LI Fujuan, GAO Guangliang. The Path and Progress of Empowering Network Attack and Defense Platforms with Artificial Intelligence[J/OL]. Telecommunications Science, 2026.
王群, 李馥娟, 高光亮. 人工智能赋能网络攻防平台的路径与进展[J/OL]. 电信科学, 2026. DOI：

WANG Qun, LI Fujuan, GAO Guangliang. The Path and Progress of Empowering Network Attack and Defense Platforms with Artificial Intelligence[J/OL]. Telecommunications Science, 2026. DOI：

摘要

网络攻防平台是支撑网络安全对抗演练、提升实战能力的关键载体。本文聚焦于人工智能技术对网络攻防平台的赋能路径与体系化构建方法，针对传统平台在动态演化、场景多样性与评估维度等方面的局限，研究融合多智能体系统、强化学习与大语言模型等关键技术，提出一种集成智能攻击模拟、自适应防御决策与自动化评估反馈的分层可配置平台架构，并设计了基于模仿学习与终身学习的层次化智能体训练机制。该平台能够动态生成高逼真攻击链、实现跨域协同防御与多维度量化评估，从而有效提升攻防演练的实战性与训练精准度。通过策略库、环境仿真及评估体系的场景化配置，该架构可灵活适配教育、科研与产业演练等多元应用需求。本文进一步剖析了平台面临的可解释性、仿真真实性、数据隐私与伦理规范等关键挑战，并从可解释人工智能、数字孪生、隐私计算及标准化协同等方面展望了未来研究方向，以期为构建下一代自适应、可持续演进的新型网络防御体系提供理论支撑与实践参考。

Abstract

Cyber range platforms serve as critical infrastructure for conducting cybersecurity exercises and enhancing operational readiness. This paper focuses on the enabling pathways and systematic methodologies for empowering such platforms through artificial intelligence (AI). To address the limitations of conventional platforms in dynamic evolution

scenario diversity

and evaluation dimensions

we integrate key technologies including multi-agent systems

reinforcement learning

and large language models. We propose a hierarchical and configurable platform architecture that incorporates intelligent attack simulation

adaptive defense decision-making

and automated evaluation and feedback. Furthermore

a layered intelligent agent training mechanism based on imitation learning and lifelong learning is designed.The proposed platform can dynamically generate high-fidelity attack chains

achieve cross-domain collaborative defense

and perform multi-dimensional quantitative evaluation

thereby significantly improving the realism and precision of cyber exercise training. Through scenario-specific configuration of strategy libraries

environment simulation

and evaluation systems

the architecture can be flexibly adapted to diverse application needs in education

scientific research

and industrial drills.This paper also examines key challenges faced by the platform

including model interpretability

simulation authenticity

data privacy

and ethical compliance. Future research directions are outlined

encompassing explainable AI

digital twins

privacy-preserving computation

and standardization collaboration. The study aims to provide theoretical support and practical guidance for building a next-generation adaptive and sustainably evolving cyber defense system.

关键词

Keywords

references

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