| 引用本文: | 郭晓利,王 月,李 斌.基于关联差异的电力信息物理系统虚假数据注入攻击检测[J].电力系统保护与控制,2025,53(21):166-177.[点击复制] |
| GUO Xiaoli,WANG Yue,LI Bin.False data injection attack detection in cyber-physical power systems based on correlation discrepancy[J].Power System Protection and Control,2025,53(21):166-177[点击复制] |
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| 摘要: |
| 为了保障智能电网的安全稳定运行，快速、准确的虚假数据注入攻击(false data injection attacks, FDIA)检测至关重要。现有基于数据驱动的FDIA检测模型主要依赖固定的判别阈值进行异常识别，但这一方法存在显著不足：攻击者可通过持续试探与分析模型响应行为，逐步调整注入攻击幅度，从而绕过检测机制，导致检测精度下降。针对这一问题，提出基于关联差异的FDIA检测模型。首先，从数据间关联的角度出发，设计基于关联差异的FDIA检测模型结构。其次，通过嵌入位置信息并引入修正因子以约束注意力作用范围，提出基于位置修正因子的先验关联提取方法。然后，结合量测数据序列的细粒度和多尺度特性，提出基于双流粒度对齐的序列关联提取方法。最后，引入拓扑关联，定义关联差异，并设计基于关联差异的对抗性判别准则推理方法，通过对抗训练放大正常与攻击量测数据的可区分度，得到判别准则。实验结果表明，所提模型相比于现有检测模型具有更高的检测准确率和鲁棒性，在面对不同幅值的注入攻击时表现优异。 |
| 关键词: 虚假数据注入攻击 关联差异 量测数据 对抗性判别准则 |
| DOI：10.19783/j.cnki.pspc.241690 |
| 投稿时间：2024-12-18修订日期：2025-03-26 |
| 基金项目:国家自然科学基金项目资助(52377081)；吉林省自然科学基金项目资助(20220101234JC) |
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| False data injection attack detection in cyber-physical power systems based on correlation discrepancy |
| GUO Xiaoli1,2,WANG Yue1,LI Bin1 |
| (1. School of Computer Science, Northeast Electric Power University, Jilin 132012, China; 2. Jilin Engineering Technology
Research Center of Intelligent Electric Power Big Data Processing, Jilin 132012, China) |
| Abstract: |
| To ensure the secure and stable operation of smart grids, fast and accurate detection of false data injection attacks (FDIA) is critical. Existing data-driven FDIA detection models primarily rely on fixed discrimination thresholds for anomaly identification. However, this approach has notable limitations: attackers can iteratively probe and analyze model responses, gradually adjusting the magnitude of injected attacks to bypass detection, thereby reducing detection accuracy. To address this issue, this paper proposes a FDIA detection model based on correlation discrepancy. First, a detection framework centered on data correlation discrepancies is designed. Second, a position-aware correction factor is embedded to constrain attention scopes, enabling prior correlation extraction with enhanced positional awareness. Then, leveraging the fine-grained and multi-scale characteristics of measurement data sequences, a dual-stream granularity alignment method is developed to capture sequential correlations. Finally, topological correlations are incorporated to define correlation discrepancies, and an adversarial discrimination criterion is formulated through adversarial training to amplify the distinguishability between normal and attacked measurements, resulting in an effective discrimination criterion. Experimental results demonstrate that the proposed model achieves superior detection accuracy and robustness compared with existing methods and performs well under injection attacks of varying magnitudes. |
| Key words: false data injection attack correlation discrepancy measurement data adversarial discrimination criteria |
