论文标题
基于自动编码器的错误数据注射攻击的培训策略
Training Strategies for Autoencoder-based Detection of False Data Injection Attacks
论文作者
论文摘要
电网中能源供应的安全性在很大程度上取决于准确估计系统状态的能力。但是，操纵的功率流量测量可能会隐藏超载并绕过不良数据检测方案，以干扰估计状态的有效性。在本文中，我们使用自动编码器神经网络来检测异常系统状态，并研究超参数对靶向功率流的错误数据注射攻击的检测性能的影响。 IEEE 118总线系统的实验结果表明，所提出的机制具有达到令人满意的学习效率和检测准确性的能力。
The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to detect anomalous system states and investigate the impact of hyperparameters on the detection performance for false data injection attacks that target power flows. Experimental results on the IEEE 118 bus system indicate that the proposed mechanism has the ability to achieve satisfactory learning efficiency and detection accuracy.
