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Deep learning-based multilabel classification for locational detection of false data injection attack in smart grids

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Abstract

With the recent advancement in smart grid technology, real-time monitoring of grid is utmost essential. State estimation-based solutions provide a critical tool in monitoring and control of smart grids. Recently there has been an increased focus on false data injection attacks which can circumvent the traditional statistical bad data detection algorithm. Most of the research methodologies focus on the presence of FDIA in measurement set, whereas their exact locations remain unknown. To cater this issue, this paper proposes a deep learning architecture for detection of the exact locations of data intrusions in real-time. This deep learning model in association with traditional bad data detection algorithms is capable of detecting both structured as well as unstructured false data injection attacks. The deep learning architecture is not dependent on statistical assumptions of the measurements, it emphasizes on the inconsistency and co-occurrence dependency of potential attacks in measurement set, thus acting as a multilabel classifier. Such kind of architecture remains model free without any prior statistical assumptions. Extensive research work on IEEE test-bench shows that this scheme is capable of identifying the locations for intrusion under varying noise scenarios. Such kind of an approach shows potential results also in detection of presence of falsified data.

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Abbreviations

AUC:

Area under curve

BDD:

Bad data detection

D-FACTS:

Distributed flexible AC transmission system

FDIA:

False data injection attack

FPR:

False positive rate

IoT:

Internet of things

KNN:

K-nearest neighbors

PCA:

Principal component analysis

ROC:

Region of convergence

RTDS:

Real-time digital simulator

SCADA:

Supervisory control and data acquisition

TPR:

True positive rate

t-SNE:

t-distributed stochastic neighbor embedding

\(u\left( {a,b} \right) \) :

Uniform distribution with mean a and standard deviation b

WLS:

Weighted least squares

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, 221005, India

    Debottam Mukherjee & Sandip Ghosh

  2. Department of Electrical Engineering, National Institute of Technology Arunachal Pradesh, Yupia, Arunachal Pradesh, 791112, India

    Samrat Chakraborty

Authors
  1. Debottam Mukherjee
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  2. Samrat Chakraborty
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  3. Sandip Ghosh
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All authors have equally contributed for analysis and interpretation of the results and data, drafting the article or revising it critically and preparing the final version.

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Correspondence to Debottam Mukherjee.

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Mukherjee, D., Chakraborty, S. & Ghosh, S. Deep learning-based multilabel classification for locational detection of false data injection attack in smart grids. Electr Eng 104, 259–282 (2022). https://doi.org/10.1007/s00202-021-01278-6

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