Energy Theft Detection in Smart Grids: Taxonomy, Comparative Analysis, Challenges, and Future Research Directions Mohsin Ahmed, Abid Khan, Mansoor Ahmed, Mouzna Tahir, Gwanggil Jeon, Giancarlo Fortino, Fellow, IEEE, and Francesco Piccialli, Member, IEEE Abstract—Electricity theft is one of the major issues in developing countries which is affecting their economy badly. Especially with the introduction of emerging technologies, this issue became more complicated. Though many new energy theft detection (ETD) techniques have been proposed by utilising different data mining (DM) techniques, state & network (S&N) based techniques, and game theory (GT) techniques. Here, a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations. Three levels of taxonomy are presented to classify state-of-the-art ETD techniques. Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature. The challenges of different ETD techniques and their mitigation are suggested for future work. It is observed that the literature on ETD lacks knowledge management techniques that can be more effective, not only for ETD but also for theft tracking. This can help in the prevention of energy theft, in the future, as well as for ETD. Index Terms—Challenges, comparative analysis, energy theft detection, future research directions, smart grid, taxonomy.    I. Introduction T HE smart city infrastructure and its services are dealing with complex challenges to comply with the necessities of residents, due to the exponential growth in the human population. It is very problematic to find a smarter way to overcome the issues of traffic congestion, waste management, increased energy consumption, and over-consumption of resources. Technology has been evolved in its way to give ease to humans and introduced the concept of the smart city where everything is interconnected with each other. Typically, it consists of smart services, smart transport, smart healthcare, smart surveillance, smart building, and smart distribution of electricity and water. A smart grid (SG) is an essential part of a smart city that is concerned with the smart management of electricity generation, transmission, distribution, and control. SG records all actions of its users – i.e., energy consumers and staff working at SG –, improves the utilisation of electricity resources and provides a long-term viable power system. These features make it of high quality, minimise overall losses, and maximise the security to ensure the safe supply of power [1]. SG also reduces the environment’s pollution by controlling carbon emissions. Smart city and SG combinedly utilise the smart service and advanced electrical engineering, assisted by information and communication technology (ICT), for effective management of complex infrastructure. There are some shared principles for the interaction between smart city and SG, such as integration, intelligent inter-connectivity, and end-user elements of the SG. Where the SG is providing ease to its stakeholders, at the same time it is giving birth to some new potential challenges. One of the crucial challenges is a threat to the security and privacy of SG, which ultimately leads to electricity theft. If fraudulent users get access to the private data of the consumers, they could use it in a wrong way and affect the lives of consumers. There is a strong need to provide a security mechanism to continue the operations of SG. Otherwise, there could be chances to shut it down. This could make its consumers face a shortage of electricity that will disturb normal life activities, such as the halt of heating systems, unavailability of online payment systems, and many others. Many researchers have shown their interest in providing security to the SG infrastructure by identifying the Manuscript received November 23, 2020; revised March 10, 2021 and April 16, 2021; accepted June 13, 2021. This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement (801522), Science Foundation Ireland and co-funded by the European Regional Development Fund through the ADAPT Centre for Digital Content Technology (13/RC/2106_P2). Recommended by Associate Editor Jun Zhang. (Corresponding author: Gwanggil Jeon.) Citation: M. Ahmed, A. Khan, M. Ahmed, M. Tahir, G. Jeon, G. Fortino, and F. Piccialli, “Energy theft detection in smart grids: Taxonomy, comparative analysis, challenges, and future research directions,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 578–600, Apr. 2022. M. Ahmed is with the Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan (e-mail: mohsin.ahmad7676 @gmail.com). A. Khan is with the Department of Computer Science, School of Computing, Engineering and Digital Technologies, Teesside University, Tees Valley TS1 3BX, United Kingdom (e-mail: abk15@aber.ac.uk). M. Ahmed is with the Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan, and also with the Innovative Value Institute, Maynooth University, Maynooth W23 F2K8, Ireland (e-mail: mansoor@comsats.edu.pk; Mansoor.ahmed@mu.ie). M. Tahir is with the Department of Computer Science, Bahria University, Lahore 54782, Pakistan (e-mail: mouzna.bulc@bahria.edu.pk). G. Jeon is with the School of Electronic Engineering, Xidian University, Xi’an 710071, China, and also with the Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea (e-mail: gjeon@inu.ac.kr). G. Fortino is with the Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, CS 87036, Italy (e-mail: g.for- tino@unical.it). F. Piccialli is with the Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Napoli 80138, Italy (e-mail: francesco.piccialli@unina.it). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JAS.2022.105404 578 IEEE/CAA JOURNAL OF AUTOMATICA SINICA, VOL. 9, NO. 4, APRIL 2022