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A Graph Neural Network Multi-Task Learning-Based Approach for Detection and Localization of Cyberattacks in Smart Grids

Citation Author(s):
Abdulrahman Takiddin, Rachad Atat, Muhammad Ismail, Katherine Davis, Erchin Serpedin
Submitted by:
Abdulrahman Takiddin
Last updated:
22 May 2023 - 5:30pm
Document Type:
Presentation Slides
Document Year:
Abdulrahman Takiddin
Paper Code:

False data injection attacks (FDIAs) on smart power grids' measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5 - 100% and 99 - 100%, respectively, presenting improvements of 5 - 30% compared to benchmarks.

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