# LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection
## 2 Literature Review  
The existing work related to ETD is classified into three categories: state-based solutions, game theory and machine learning [13]. In particular, the state-based solutions focus on designing specific metering devices and distribution transformers to detect electricity theft [14]. Through the state-based solution, a high detection accuracy can be achieved. However, these methods require additional hardware tools such as meter sensors and distribution transformers, which have a high cost. The game theory-based solutions [15; 16] assume that there is a game between the energy thief and electric utilities. The electricity theft is detected according to the difference between the distribution of \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Limitation Number** & **Limitation Identified** & **Solution Number** & **Proposed Solution** \\ \hline L.1 & Imbalanced data & S.1 & RUSBoost \\ \hline L.2 & Missing values and outliers & S.2 & Interpolation and normalization \\ \hline L.3 & Overfitting & S.3 & LSTM and RUSBoost \\ \hline L.4 & High-dimensional data & S.4 & LSTM \\ \hline L.5 & Parameter optimization & S.5 & Bat optimization \\ \hline L.6 & Reliable Evaluation & S.6 & Precision, Recall, F1-Score, ROC \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mapping of problems addressed and proposed solution. RUSBoost: bat-based random under-sampling boosting; LSTM: long short term memory; ROC: receiver operating characteristics. electricity consumption that is derived from a game outcome. The game theory-based solutions have a low cost to detect electricity theft. However, it is necessary to define the utility functions for all players in a game, which is time consuming. The machine learning-based solutions use electricity consumption data to analyze the load profiles of the consumers in order to find benign users. The approaches are further categorized into clustering, semi-supervised and supervised techniques. The clustering techniques can be applied to an unlabelled dataset and rely solely on outlier detection. The authors in [17] presented an unsupervised learning method based on K-nearest neighbors (KNN) to separate anomalous consumption from normal patterns. In semi-supervised learning, both labelled and unlabelled datasets are used for the detection of electricity theft. The authors in [18] proposed a semi-supervised learning technique called the stacked sparse auto-encoder (SSAE) for the detection of NTL in a smart grid. The contributions and drawbacks of the existing techniques are mentioned in Table 2.  
The aforementioned methods are related to state-based, game theory and unsupervised machine learning techniques. Our approach proposes a solution based on supervised learning. Therefore, we will study the recent advances made in this area in detail. Some recent studies [19; 20] have been based on SVM and LR. The main idea of these methods is to classify honest customers and electricity thieves. The authors in [21] addressed NTL detection in a power distribution system using maximum overlap packet transform (MODWPT) and RUSBoost techniques. The MODWPT method is utilized to extract the relevant features from input data while RUSBoost performs the final classification. In [22], the authors proposed a model based on DT and SVM to detect malicious consumers that intentionally steal electricity; however, no reliable performance metric was used. In recent times, deep learning approaches have been used in areas such as natural language processing and image recognition. Deep learning techniques are also used to build models to work with the massive data arising from smart meters. They have the ability to learn from huge amounts of data and perform better feature extraction and classification processes. The summary of the existing literature related to supervised machine learning techniques for ETD is presented in Table 3.  
[PERSON] et al. [23] proposed a hybrid neural network composed of LSTM and the multi-layer perceptron (MLP) for ETD.