## 1 Introduction  
In the modern world, electricity utilization is increasing day by day. It is broadly categorized into six main areas. These areas are residential, industrial, commercial, traction, agriculture, and other activities [1]. More than 65% of energy is consumed by residential regions [2]. Traditional grid is replaced with smart grid because it has some limitations, i.e., one-way communication, manual monitoring and restoration, central distribution with few sensors, etc. [3], whereas in smart grid, the information flows in two ways between the utility and the consumer [4; 5]. It also helps utilities to produce electricity according to the customer's needs [6; 7; 8]. Electricity losses take place during generation, transmission, and distribution. There are two types of losses: technical and non-technical [9]. Technical losses occur in the electrical system by internal actions, for example, problem in the transformer or issue in the transmission lines [10]. Non-technical Losses (NTL) occur in the electrical system by external actions, for example unknown and incorrect flow of electricity, inaccurate meter readings, non-payment of bills by customers, and errors in maintaining database records [11]. Electricity theft is one of the major causes of NTL. There are different types of electricity theft including meter tampering or by passing, smart meter hacking, etc. [12]. Different types of issues take place due to electricity theft like revenue loss, electricity surging, and heavy load on electrical systems [13]. According to [PERSON] et al. [14], one-hundred million Canadian dollars are wasted every year as a result of electricity theft. This loss of power supply can be enough for 77,000 users for a year. We have mapped problems with proposed solutions as shown in Table 1.  
Recently, many authors proposed different approaches to solve these issues, which are broadly classified into three main categories: Artificial Intelligence (AI) and Machine Learning (ML)-based, State-based, and game theory-based systems. State-based approaches observe the structure in which information is collected from different resources. However, the additional cost of hardware devices is required to implement the model. Moreover, on site inspection is used to detect electricity theft. However, it is not possible to inspect each user within a short period of time. In game theory-based approaches, there is a game between utility and electricity theft [15]. However, these approaches have high False Positive Rate (FPR) and low detection rate. The most challenging problem in game theory based solutions is to defined the rules and interaction between players. On the other hand, the main focus of machine learning and artificial intelligence based systems is to analyze the patterns of real time series data. These systems extract useful information from a dataset by analyzing electricity consumption patterns [16]. Any deviation or changes in the consumption patterns may lead to electricity fraud case or illegal action [17; 18]. Additional hardware devices are required to detect theft, and these devices have high maintenance cost. Domain experts are required for data analysis and final decision-making. Therefore, there is a need to develop automated electricity theft detection method to overcome these issues [14]. Figure 1 shows the normal and abnormal consumption of energy in two months (i.e., August and September 2016). The main contributions of this research are summarized as follows. * The problem of imbalanced data is solved by employing the Smote Over Sampling Tomik Link (SOSTLink) sampling method.