Fast economic dispatch with false data injection attack in electricity-gas cyber–physical system: A data-driven approach

Gao, Xiaxiang; Yang, Xiyun; Meng, Lingzhuochao; Wang, Shuyan

doi:10.1016/j.isatra.2022.12.009

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…The evaluation of the introduced attack detector encompasses two viewpoints: the assessment of detection loss probability and false alarm probability. The data-driven approach introduced in [20] demonstrates the integration of residual networks and attention-mechanism-based long short-term memory models. This integration is directed toward the achievement of temporal correlation and feature extraction in measurement data.…”

Section: Fdia Detection Based On a Data-driven Approachmentioning

confidence: 99%

The Detection of False Data Injection Attack for Cyber–Physical Power Systems Considering a Multi-Attack Mode

Zhou,

Li,

Zang

et al. 2023

Applied Sciences

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Amidst the evolving communication technology landscape, conventional distribution networks have gradually metamorphosed into cyber–physical power systems (CPPSs). Within this transformative milieu, the cyber infrastructure not only bolsters grid security but also introduces a novel security peril—the false data injection attack (FDIA). Owing to the variable knowledge held by cyber assailants regarding the system’s network structure, current achievements exhibit deficiencies in accommodating the detection of FDIA across diverse attacker profiles. To address the historical data imbalances encountered during practical FDIA detection, we propose a dataset balancing model based on generating adversarial network-gated recurrent units (GAN-GRU) in conjunction with an FDIA detection model based on the Transformer neural network. Harnessing the temporal data extraction capabilities of gated recurrent units, we construct a GRU neural network system as the GAN’s generator and discriminator, aimed at data balance. After preprocessing, the balanced data are fed into the Transformer neural network for training and output classification to discern distinct FDIA attack types. This model enables precise classification amidst varying FDIA scenarios. Validation involves testing the model on load data from the IEEE 118-bus system and affirming its high accuracy and effectiveness in detecting power systems after multiple attacks.

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Section: Fdia Detection Based On a Data-driven Approachmentioning

confidence: 99%