Smart meters are a vital part of the smart grid, enabling energy management, real-time control, and data collection. Despite advances in technology, there is still a lack of content and limited understanding of the specific cybersecurity threats facing these devices, as well as the effectiveness of existing mitigation strategies. This study analyzed 41 articles sourced from three academic databases (Scopus, Web of Science, and IEEE Xplore). A cutting-edge study was conducted, including a comprehensive review of relevant literature on smart meters, cybersecurity vulnerabilities, and mitigation strategies. Elements were selected based on pre-assessment and classification processes, and the data were extracted and combined to provide detailed insights into the new devices. The study identified several significant cybersecurity risks for smart meters, including data breaches, unauthorized access, data manipulation, denial-of-service (DoS) attacks, and malware introduction. The study also highlighted the vulnerabilities exploited by these threats, such as undocumented communications, weak authentication, and outdated software. Recommended mitigation strategies include strengthening access and authentication mechanisms, securing communication systems, regular software updates, code management, anomaly detection, and access control. The findings indicate that although there are good strategies and methods to mitigate these cyber threats, significant research gaps remain. These gaps include design requirements, software and firmware updates, physical security, the use of big data to detect vulnerabilities, user data privacy, and inconsistencies in machine learning algorithms. Future research should focus on these aspects to improve the stability and reliability of smart meters.

This study provides a scientometric analysis of research focused on energy theft detection and load profiling in smart grid networks. Data were retrieved from the Web of Science and Scopus databases, covering publications from 2003 to April 2024. Using the Bibliometrix package and VOSviewer software, we analyzed trends in publications, author productivity, collaborative networks, and key journals. The study highlights significant growth in the research field, with China and the USA emerging as the most productive countries, with strong international collaboration. Nadeem Javaid is identified as a leading author, contributing to publications with a strong focus on the application of deep learning techniques for energy consumption analysis in smart grids. Key journals such as IEEE Access, Applied Energy, and Energies were found to be central to this research area. Our findings highlighted the importance of this area, as smart grid technologies continue to evolve, requiring advanced methodologies to detect non-technical losses and analyze consumption patterns. This research supports the United Nations’ (UN) Sustainable Development Goals (SDGs), particularly goals related to sustainable energy and infrastructure development, by emphasizing the importance of technological innovation and collaboration in tackling energy theft.

Smart meters are a vital part of the smart grid, enabling energy management, real-time control, and data collection. Despite advances in technology, there is still a lack of content and limited understanding of the specific cybersecurity threats facing these devices, as well as the effectiveness of existing mitigation strategies. This study analyzed 41 articles sourced from three academic databases (Scopus, Web of Science and IEEE Xplore). A cutting-edge study was conducted, including a comprehensive review of relevant literature on smart meters, cybersecurity vulnerabilities, and mitigation strategies. Elements were selected based on pre-assessment and classification processes, and the data were extracted and combined to provide detailed insights into the new devices. The study identified several significant cybersecurity risks for smart meters, including data breaches, unauthorized access, data manipulation, denial of service (DoS) attacks, and malware introduction. The study also highlighted the vulnerabilities exploited by these threats, such as undocumented communications, weak authentication, and outdated software. Recommended mitigation strategies include strengthening access and authentication mechanisms, securing communication systems, regular software updates, code management, anomaly detection, and access control. The findings indicate that, although there are good strategies and methods to mitigate these cyber threats, significant research gaps remain. These gaps include design requirements, software and firmware updates, physical security, the use of big data to detect vulnerabilities, user data privacy, and inconsistencies in machine learning algorithms. Future research should focus on these aspects to improve the stability and reliability of smart meters.

This article describes a compact implementation in hardware of S-Boxes for Twofish Algorithm in Field Programmable Gate Array - one of the Advanced Encryption Standard (AES) finalists. The core was implemented using Altera Quartus Cyclone board. The code is totally portable and can be used in any FPGA family because it was developed using the VHDL patterns. The function was implemented for 128-bit word and 128, 192 and 256-bit key. The main goal of this work was the implementation of a compact and modular S-Box module for Twofish algorithm that can find a wide range of applications and that can be choosen as an alternative from AES-Rijndael.

O objetivo deste trabalho é analisar como o uso da gamificação pode favorecer o ensino/aprendizado da computação para alunos de cursos médio/técnico-integrado que não foram previamente alfabetizados na área. A estratégia adotada foi utilizar jogos digitais na sala de aula e investigar como esses auxiliam os alunos na construção de modelos mentais sobre os conceitos importantes ao pensamento computacional. No experimento foi utilizado o jogo TIS-100 da Zachtronics. A investigação sobre os modelos mentais foi realizada através de avaliações e observações de diálogos verbalizados pelos alunos durante as aulas. Concluímos que a gamificação é uma estratégia poderosa para auxiliar no desenvolvimento do pensamento computacional.