Title

Detecting Dynamic Attacks in Smart Grids Using Reservoir Computing: A Spiking Delayed Feedback Reservoir Based Approach

Document Type

Article

Publication Date

6-1-2020

Department

Department of Electrical and Computer Engineering

Abstract

Spiking neural networks have been widely used for supervised pattern recognition exploring the underlying spatio-temporal correlation. Meanwhile, spatio-temporal correlation manifests significantly between different components in a smart grid making the spiking neural network a desirable candidate for false data injection attack detection. In this paper, we develop a spiking-neural-network-based technique for dynamic cyber-attack detection in a smart grid. This is achieved through judiciously integrating spiking neurons with a special recurrent neural network called the delayed feedback reservoir computing. The inter-spike interval encoding is also explored in the precise-spike-driven synaptic plasticity based training process. The simulation results suggest that the introduced method outperforms multi-layer perceptrons and can achieve a significantly better performance compared to the state-of-the-art techniques. Furthermore, our analysis indicates that the delay value in the delayed feedback reservoir will have a substantial impact on the overall system performance.

Publisher's Statement

© 2017 IEEE. Publisher’s version of record: https://doi.org/10.1109/TETCI.2019.2902845

Publication Title

IEEE Transactions on Emerging Topics in Computational Intelligence

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