A GAN-based Adversarial Testing Method for Edge Intelligence in Power Internet of Things via Digital Twin Framework

Liu Siyang; Li Bo; Zhu Mengyao; Tan Biao; Zuo Hong; He Jiapeng

doi:10.11959/j.issn.1000-0801.DXKX250682

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A GAN-based Adversarial Testing Method for Edge Intelligence in Power Internet of Things via Digital Twin Framework

更新时间：2026-05-19

- A GAN-based Adversarial Testing Method for Edge Intelligence in Power Internet of Things via Digital Twin Framework
- Telecommunications Science (2026)
- 作者机构：
  
  1.云南电网有限责任公司电力科学研究院，云南省昆明市 650217
  云南省绿色能源与数字电力量测及控保重点实验室，云南省昆明市 650217
  3.云南电网有限责任公司怒江供电局，云南省怒江市 673100
  4.输变电装备技术全国重点实验室（重庆大学）,重庆市,400044
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX250682
  CLC：
- Received：26 November 2025，
  
  Revised：2026-03-08，
  
  Accepted：18 May 2026，
- 稿件说明：
移动端阅览
Liu Siyang, Li Bo, Zhu Mengyao, et al. A GAN-based Adversarial Testing Method for Edge Intelligence in Power Internet of Things via Digital Twin Framework[J/OL]. Telecommunications Science, 2026.
DOI：

Liu Siyang, Li Bo, Zhu Mengyao, et al. A GAN-based Adversarial Testing Method for Edge Intelligence in Power Internet of Things via Digital Twin Framework[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX250682.

摘要

针对电力物联网边缘节点面临的复杂对抗攻击识别难、实时性要求高及资源约束严等问题，提出一种基于GAN数字孪生框架的边缘智能对抗性测试方法。首先构建融合物理约束与时序一致性的GAN模型，生成符合边缘设备运行规律的对抗样本，结合数字孪生技术搭建边缘节点虚拟镜像，实现攻击场景的精准复现；其次设计常规工况、单一对抗、混合对抗三类测试场景，建立包含检测准确率、响应时延、虚假阳性率及算力占用率的四维评价体系；最后，案例分析结果表明，该方法在混合攻击场景下检测准确率达89.5%，响应时延控制在3.3-5.2ms，虚假阳性率低至0.5%，算力占用维持在22%-23%区间，较传统规则匹配法与单一机器学习模型，在复杂攻击识别能力、实时性及资源适配性上均有显著提升，为电力物联网边缘智能安全测试提供了高效可行的解决方案。

Abstract

To address the challenges faced by edge nodes in the Power Internet of Things (PIoT)

such as difficulty in identifying complex adversarial attacks

high real-time requirements

and strict resource constraints

an adversarial testing method for edge intelligence based on a GAN-driven digital twin framework is proposed. Firstly

a GAN model integrating physical constraints and temporal consistency is constructed to generate adversarial samples that conform to the operating rules of edge devices. Combined with digital twin technology

a virtual mirror of edge nodes is built to achieve accurate reproduction of attack scenarios. Secondly

three types of test scenarios—normal operating conditions

single adversarial attacks

and mixed adversarial attacks—are designed

and a four-dimensional evaluation system including detection accuracy

response delay

false positive rate

and computing power occupancy rate is established. Finally

case analysis results show that the proposed method achieves a detection accuracy of 89.5% in mixed attack scenarios

controls the response delay within the range of 3.3–5.2 ms

reduces the false positive rate to as low as 0.5%

and maintains the computing power occupancy between 22% and 23%. Compared with traditional rule-based matching methods and single machine learning models

it exhibits significant improvements in complex attack identification capability

real-time performance

and resource adaptability

providing an efficient and feasible solution for the security testing of edge intelligence in the PIoT.

关键词

Keywords

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