Electrical theft is a big issue in the energy industry, as it harms electric power suppliers and results in financial loss. Detecting and preventing electrical theft is a difficult undertaking that requires consideration of a variety of factors, including economic, social, regional, managerial, political, infrastructure, literacy rate, and so on.

Smart power meter deployments via modern metering infrastructure offer intriguing solutions and even more potential, as they give sufficient data for analytical inferences and pre-emptive cyber-attack mitigation.

The first step in preventing energy thefts, according to this study, is to identify the sources of hazards. It provides a framework for monitoring, recognising, and mitigating threats in a smart utility network based on factors suggestive of electricity thefts. The proposed framework focuses primarily on the symptoms of the identified dangers that are indicative of potential electricity theft.

The losses in power systems network are mainly categorised into technical losses, comprising the power dissipated in power systems' components (e.g., transmission and distribution lines, transformers, protective devices etc.) and non-technical losses, which are losses that cannot be traced back to any technological components. technical losses occur naturally and are often calculated based on the systems' components and network parameters while electricity thefts form the major lump of the non-technical losses. As a result, energy thefts and non-technical losses are frequently used interchangeably.

This may be addressed by providing real-time monitoring of power use and billing data for the purposes of analysing usage trends and detecting anomalies in order to ensure energy efficiency.

AMI provides a comprehensive picture of how to address the many security issues that lead to electricity theft. Future research could go deeper into each of the highlighted anomalies, as well as the use of appropriate intelligent algorithms to test data in order to build the framework for real-time decision assistance.