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Edge computing enabled non-technical loss fraud detection for big data security analytic in Smart Grid

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Abstract

With the fast development of Smart Grid globally, the security issues arise sharply and Non-Technical Loss (NTL) fraud is one of the major security issues. There are some existing NTL fraud detectors, however, when big data security challenges emerge in Smart Grid, none of them can detect NTL fraud for big data in Smart Grid. In this paper, we propose ENFD, an NTL detection scheme enabled by Edge Computing and big data analytic tools to address big data NTL fraud detection problem in Smart Grid. The research work provides us with experience of developing big data security solutions in Smart Grid. The experimental results show that ENFD can efficiently detect big data NTL frauds which cannot be detected by the state-of-the-art detectors. ENFD can detect small data NTL frauds as well and the average detection speed is about six to seven times that of the fastest detector exists in the literature.

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Authors and Affiliations

  1. Department of Computer Science, California State University, Fullerton, CA, 92831, USA

    Wenlin Han

  2. Navigation College, Dalian Maritime University, 1 Linghai Road, Gaoxinyuan District, Dalian, China

    Yang Xiao

  3. Department of Computer Science, The University of Alabama, Tuscaloosa, AL, 35487-0290, USA

    Yang Xiao

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  1. Wenlin Han
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  2. Yang Xiao
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Correspondence to Yang Xiao.

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Han, W., Xiao, Y. Edge computing enabled non-technical loss fraud detection for big data security analytic in Smart Grid. J Ambient Intell Human Comput 11, 1697–1708 (2020). https://doi.org/10.1007/s12652-019-01381-4

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