  Hacking and false data injection from adversaries can threaten power grids'
everyday operations and cause significant economic loss. Anomaly detection in
power grids aims to detect and discriminate anomalies caused by cyber attacks
against the power system, which is essential for keeping power grids working
correctly and efficiently. Different methods have been applied for anomaly
detection, such as statistical methods and machine learning-based methods.
Usually, machine learning-based methods need to model the normal data
distribution. In this work, we propose a novel anomaly detection method by
modeling the data distribution of normal samples via multiple encoders and
decoders. Specifically, the proposed method maps input samples into a latent
space and then reconstructs output samples from latent vectors. The extra
encoder finally maps reconstructed samples to latent representations. During
the training phase, we optimize parameters by minimizing the reconstruction
loss and encoding loss. Training samples are re-weighted to focus more on
missed correlations between features of normal data. Furthermore, we employ the
long short-term memory model as encoders and decoders to test its
effectiveness. We also investigate a meta-learning-based framework for
hyper-parameter tuning of our approach. Experiment results on network intrusion
and power system datasets demonstrate the effectiveness of our proposed method,
where our models consistently outperform all baselines.
