| 引用本文: | 李 卓,谢耀滨,吴茜琼,等.基于深度学习的电力系统虚假数据注入攻击检测综述[J].电力系统保护与控制,2024,52(19):175-187.[点击复制] |
| LI Zhuo,XIE Yaobin,WU Qianqiong,et al.Review of deep learning-based false data injection attack detection in power systems[J].Power System Protection and Control,2024,52(19):175-187[点击复制] |
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| 摘要: |
| 虚假数据注入攻击(false data injection attack, FDIA)是针对电力系统的一种常见网络攻击，可以通过终端链路或设备注入异常数据，绕过不良数据检测机制，进而引发电力系统的异常运行，造成严重的经济损失。近年来深度学习技术在FDIA检测方面取得诸多进展，通过大量的数据训练和强大的模型学习能力，能够自动学习和提取攻击数据特征，相对于传统方法具有更高的准确率和鲁棒性。总结了近年来基于深度学习的电力系统FDIA检测研究进展，涵盖卷积神经网络、循环神经网络、图神经网络、生成对抗网络和深度强化学习等典型深度学习模型。首先分析各类深度学习模型的FDIA检测原理，并介绍相关技术方法。然后从鲁棒性、评估指标和可扩展性等方面对上述技术进行对比分析，总结其应用范围及存在不足。最后探讨了当前研究中存在的挑战和未来的研究发展方向。 |
| 关键词: 虚假数据注入 攻击检测 深度学习 电力系统安全 |
| DOI：10.19783/j.cnki.pspc.231632 |
| 投稿时间：2023-12-21修订日期：2024-01-25 |
| 基金项目:国家青年科学基金项目资助“基于文件操作特征分析的智能移动终端数据安全研究”(62372465) |
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| Review of deep learning-based false data injection attack detection in power systems |
| LI Zhuo1,XIE Yaobin1,WU Qianqiong1,ZHANG Youwei2 |
| (1. Information Engineering University, Zhengzhou 450000, China; 2. Zhengzhou Xinda Advanced
Technology Research Institute, Zhengzhou 450000, China) |
| Abstract: |
| False data injection attack (FDIA) is a common network attack targeting power systems. These attacks can cause abnormal operation of the power system and result in serious economic losses by injecting abnormal data through terminal links and devices which can bypass bad data detection mechanisms. In recent years, deep learning technology has made significant progress in FDIA detection. Deep learning methods can automatically learn and extract attack features with a large amount of data training and powerful learning capabilities. These methods have higher accuracy and robustness than traditional methods. The paper summarizes the research on deep-learning-based power systems FDIA detection technologies in recent years, and covers typical deep learning models such as convolutional, recurrent and graph neural networks, generative adversarial networks, and deep reinforcement learning. First, the FDIA detection principles of various deep learning models and relevant technical methods are analyzed and introduced. Then, a comparative analysis of the above-mentioned technologies in terms of robustness, evaluation indicators, and scalability is proposed to summarize their application scope and shortcomings. Finally, the challenges in current and future research directions are discussed. |
| Key words: false data injection attack detection deep learning power system security |
