AUTHOR=Munawar Shoaib , Khan Zeshan Aslam , Chaudhary Naveed Ishtiaq , Javaid Nadeem , Raja Muhammad Asif Zahoor , Milyani Ahmad H. , Azhari Abdullah Ahmed 

TITLE=Novel FDIs-based data manipulation and its detection in smart meters’ electricity theft scenarios

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.1043593

DOI=10.3389/fenrg.2022.1043593

ISSN=2296-598X

ABSTRACT=Non technical loss (NTL) is a serious issue around the globe. Consumers manipulate their smart
meters (SMs) data in order to under report their readings and gain financial benefits. Various
manipulation techniques are used for gaining such unfair manipulation. This paper highlights
novel false data injection (FDIs) techniques, which are used to manipulate the SMs data. These
techniques are introduced in comparison to six theft cases. Furthermore, various features are
engineered to analyze the variance, complexity and distribution of the manipulated data. The
variance and complexity is created in data distribution when FDIs and theft cases are used to
poison the SMs data, which is investigated through skewness and kurtosis analysis. Furthermore,
to tackle the data imbalance issue, proximity weighted synthetic oversampling (ProWsyn) technique
is used. Moreover, a hybrid attentionLSTMInception is introduced, which is an integration of
attention layers, LSTM and inception blocks to tackle data dimensionality, misclassification and
high false positive rate (FPR) issues. The proposed hybrid model outperforms the traditional theft
detectors and achieves an accuracy of 0.95%, precision 0.97%, recall 0.94%, F1 score 0.96%
and area under the curve (AUC) score 0.98%.