# LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection
## 2 Literature Review
The MLP is used to integrate auxiliary information, while LSTM is used to extract relevant information from the sequential data. The authors in [24] detected electricity theft by extracting the local and global features through a wide and deep CNN. The wide component was used to capture the global features from 1D data, while the deep component was used to capture periodicity from 2D data. [PERSON] et al. [25] contributed to the detection of electricity theft by proposing a hybrid model composed of CNN and LSTM. To counter imbalanced datasets, the authors used the synthetic minority over-sampling technique (SMOTE). However, SMOTE generates synthetic data, which causes overfitting and deviates from realistic theft cases. [PERSON] et al. [26] developed a hybrid model composed of CNN and random forest (RF) for fraud detection in smart grids; CNN was used to automatically extract features from customers' consumption data, while RF was used to perform the final classification. \begin{table}
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**Methods** & **Contributions** & **Limitations** \\ \hline State-based [14] & State-based solution has achieved a high detection accuracy for electricity theft & High cost of hardware installation \\ \hline Game theory [15; 16] & Game theory-based solutions have a low cost for finding electricity theft & It is necessary to define the utility function for all players in a game, which is time-consuming \\ \hline Machine learning [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28] & A data-driven approach is used to effectively detect anomalous consumption behavior & Performance degrades with imbalanced data \\ \hline \hline \end{tabular}
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Table 2: Contributions and limitations of defense techniques against electricity theft. The CNN-RF model exhibited very good performance on several performance metrics; i.e., it achieved an area under the curve (AUC) of 97.1% and recall of 96.9%. In [27], an auto encoder was proposed for the detection of anomalies in electricity consumption data using one-dimensional time series data. However, an auto encoder requires hyper-parameters tuning for training. [PERSON] et al. [28] presented a real-time theft detection approach based on the Gaussian mixture model (GMM) and LSTM. The authors used time series data and enhanced the internal structure of LSTM. The technique was validated in a low-dimensional space and achieved excellent results in terms of the F1-score. Further research is needed to adequately solve the problems of ETD and overcome the challenges of the poor detection of theft due to imbalanced data and the limited capability of ML algorithms. From the existing literature, we have found that only a few papers have considered the effects of imbalanced data in their system models. The authors in the literature addressed the class imbalance problem by utilizing SMOTE; however, this causes overfitting and replicates the nearest neighbor's samples, which do not reflect real world theft cases. In this paper, we used the RUSBoost method to deal with the imbalanced data. In addition, the traditional machine learning algorithms are used for  
\begin{table}
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**Techniques** & **Dataset** & **Contributions** & \begin{tabular}{c} **Validation** \\ **Metrics** \\ \end{tabular}  & **Limitations** \\ \hline \multirow{4}{*}{SVM, DT and LR [19; 20]} & \multirow{4}{*}{Smart meter data} & To detect the malicious consumers that intentionally steal electricity & Accuracy & No reliable performance metric is used \\ \cline{1-1} \cline{3-5}  & & & & \\ \cline{1-1} \cl  
the classification of ETD; i.e., SVM and LR. However, these methods fail to capture the consumption pattern of large time series data and result in overfitting. Moreover, deep learning techniques have become dominant for capturing real theft cases and have performed better with high-dimensional data. This encouraged us to exploit an LSTM technique to to achieve generalized performance.