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Modern advances in sensor, computing, and communication technologies enablevarious smart grid applications. The heavy dependence on communicationtechnology has highlighted the vulnerability of the electricity grid to falsedata injection (FDI) attacks that can bypass bad data detection mechanisms.Existing mitigation in the power system either focus on redundant measurementsor protect a set of basic measurements. These methods make specific assumptionsabout FDI attacks, which are often restrictive and inadequate to deal withmodern cyber threats. In the proposed approach, a deep learning based frameworkis used to detect injected data measurement. Our time-series anomaly detectoradopts a Convolutional Neural Network (CNN) and a Long Short Term Memory (LSTM)network. To effectively estimate system variables, our approach observes bothdata measurements and network level features to jointly learn system states.The proposed system is tested on IEEE 39-bus system. Experimental analysisshows that the deep learning algorithm can identify anomalies which cannot bedetected by traditional state estimation bad data detection.

Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning