Vector Auto-Regression-Based False Data Injection Attack Detection Method in Edge Computing Environment
Source of Publication
With the wide application of advanced communication and information technology, false data injection attack (FDIA) has become one of the significant potential threats to the security of smart grid. Malicious attack detection is the primary task of defense. Therefore, this paper proposes a method of FDIA detection based on vector auto-regression (VAR), aiming to improve safe operation and reliable power supply in smart grid applications. The proposed method is characterized by incorporating with VAR model and measurement residual analysis based on infinite norm and 2-norm to achieve the FDIA detection under the edge computing architecture, where the VAR model is used to make a short-term prediction of FDIA, and the infinite norm and 2-norm are utilized to generate the classification detector. To assess the performance of the proposed method, we conducted experiments by the IEEE 14-bus system power grid model. The experimental results demonstrate that the method based on VAR model has a better detection of FDIA compared to the method based on auto-regressive (AR) model.
False data injection attack (FDIA), Vector auto-regression (VAR), Attack detection, Smart grid
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Chen, Yi; Hayawi, Kadhim; Zhao, Qian; Mou, Junjie; Yang, Ling; Tang, Jie; Li, Qing; and Wen, Hong, "Vector Auto-Regression-Based False Data Injection Attack Detection Method in Edge Computing Environment" (2022). All Works. 5386.
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Open Access Type
Gold: This publication is openly available in an open access journal/series