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Conference on Complex, Intelligent, and Software Intensive Systems

CISIS 2021: Complex, Intelligent and Software Intensive Systems pp 395–405Cite as

Synthetic Theft Attacks Implementation for Data Balancing and a Gated Recurrent Unit Based Electricity Theft Detection in Smart Grids

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 278)

Abstract

In this paper, we present a novel approach for the electricity theft detection (ETD). It comprises of two modules: (1) implementations of the six theft attacks for dealing with the data imbalanced issue and (2) a gated recurrent unit (GRU) to tackle the model’s poor performance in terms of high false positive rate (FPR) due to some non malicious reasons (i.e., drift). In order to balance the data, the synthetic theft attacks are applied on the smart grid corporation of China (SGCC) dataset. Subsequently, once the data is balanced, we pass the data to the GRU for ETD. As the GRU model stores and memorizes a huge sequence of the data by utilizing the balanced data, so it helps to detect the real thieves instead of anomaly due to drift. The proposed methodology uses electricity consumption (EC) data from SGCC dataset for solving ETD problem. The performance of the adopted GRU with respects to ETD accuracy is compared with the existing support vector machine (SVM) using various performance metrics. Simulation results show that SVM achieves 64.33% accuracy; whereas, the adopted GRU achieves 82.65% accuracy for efficient ETD.

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Author information

Authors and Affiliations

  1. COMSATS University Islamabad, Islamabad, 44000, Pakistan

    Pamir, Ashraf Ullah, Muhammad Asif, Benish Kabir & Nadeem Javaid

  2. International Islamic University Islamabad, Islamabad, 44000, Pakistan

    Shoaib Munawar

Authors
  1. Pamir
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  2. Ashraf Ullah
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  3. Shoaib Munawar
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  4. Muhammad Asif
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  5. Benish Kabir
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  6. Nadeem Javaid
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Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Prof. Leonard Barolli

  2. Department of Information Security Engineering, Soonchunhyang University, Asan, Korea (Republic of)

    Dr. Kangbin Yim

  3. Faculty of Bussiness Administration, Rissho University, Tokyo, Japan

    Dr. Tomoya Enokido

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Pamir, Ullah, A., Munawar, S., Asif, M., Kabir, B., Javaid, N. (2021). Synthetic Theft Attacks Implementation for Data Balancing and a Gated Recurrent Unit Based Electricity Theft Detection in Smart Grids. In: Barolli, L., Yim, K., Enokido, T. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2021. Lecture Notes in Networks and Systems, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-79725-6_39

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