  Modern advances in sensor, computing, and communication technologies enable
various smart grid applications. The heavy dependence on communication
technology has highlighted the vulnerability of the electricity grid to false
data injection (FDI) attacks that can bypass bad data detection mechanisms.
Existing mitigation in the power system either focus on redundant measurements
or protect a set of basic measurements. These methods make specific assumptions
about FDI attacks, which are often restrictive and inadequate to deal with
modern cyber threats. In the proposed approach, a deep learning based framework
is used to detect injected data measurement. Our time-series anomaly detector
adopts a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM)
network. To effectively estimate system variables, our approach observes both
data measurements and network level features to jointly learn system states.
The proposed system is tested on IEEE 39-bus system. Experimental analysis
shows that the deep learning algorithm can identify anomalies which cannot be
detected by traditional state estimation bad data detection.
