Dynamic Detection of False Data Injection Attack in Smart Grid using Deep Learning
This addresses cybersecurity vulnerabilities in smart grids, offering a dynamic detection method for false data injection attacks, though it appears incremental as it builds on existing deep learning techniques for a specific domain.
The paper tackles the problem of detecting false data injection attacks in smart grids, which bypass traditional bad data detection, by proposing a deep learning framework using CNN and LSTM networks that jointly learns from data measurements and network features, achieving detection of anomalies not identified by conventional methods on the IEEE 39-bus system.
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.