1. Introduction
Advances in technology and the digitalization of energy systems have made smart grids an innovative strategy to maximize the amount of electricity consumed and distributed. While technological advancements and the digitalization of energy systems are promising, there are also technical challenges. One of these challenges is cybersecurity, especially when it comes to protecting the data generated by smart meters. Smart meters collect detailed information about energy use. This information is useful for improving distribution and efficiency, but it can also be an attractive target for cybercriminals. Therefore, protecting this information is very important. That is why protecting this sensitive information is of utmost importance. The expectation is that the use of smart meters by the end of 2023 will become increasingly more of a priority. There are around 29.5 million smart meters in operation in the UK [
1]. In the United States, the number is even higher, with around 128 million smart meters by the end of 2023. However, this technological advancement is not without competition [
2].
Cybersecurity has become a major issue, especially when it comes to protecting data collected by smart meters. The relationship between various devices and systems also poses significant problems. The lack of common standards can hinder effective communication between different devices and systems. Connecting these devices to networked communications exposes systems to multiple threats and highlights the importance of a secure cybersecurity strategy to ensure data integrity and known and owned privacy [
3]. The introduction of smart meters represents a major advancement in energy consumption management and monitoring, providing a detailed overview of electricity consumption in real time. However, collecting and transmitting sensitive data through these devices creates vulnerabilities that can be exploited by hackers. Therefore, adequately protecting this information becomes an urgent task to ensure the reliability and security of the smart grid [
4].
The Systematic Mapping Review of cyber threats to smart meters in smart grids is proposed in this paper. The major objectives are to identify the most significant cyber threats to smart meter data, examine advanced metering infrastructure vulnerabilities that could be used in cyberattacks, evaluate the efficacy of cybersecurity protocols and measures, identify areas in need of more research, and identify gaps in the body of existing literature.
This work was structured into eight sections.
Section 2, Related Work, reviewed previous research on the topic.
Section 3 described the Research Methodology, including the criteria used to select and compare different articles.
Section 4 presented and discussed the Main Findings.
Section 5 addressed the Research Questions.
Section 6 identified Gaps and suggested Future Research Directions. Finally,
Section 7, Emerging Cyber Threats, explored new risks posed by AI-based attacks and quantum computing, assessing their potential impact on smart grid security. Finally,
Section 8, Conclusion, summarized the principal findings and outlined strategic recommendations for strengthening cybersecurity in smart meter infrastructures.
2. Related Works
Different studies on Systematic Mapping Reviews (SMRs) and Systematic Literature Reviews (SLRs) have been conducted in the field of cybersecurity for smart meters. In this section, we review relevant research addressing similar topics.
As mentioned in [
5], it addresses cybersecurity vulnerabilities in smart grids, highlighting risks such as user awareness deficiencies and unauthorized access. It proposes mitigation techniques, including consumer education, system protection, and multi-factor authentication. The conclusion emphasizes the need for a collaborative approach to tackle security challenges during the transition to smart grids.
In [
6], a literature review is conducted on key technologies for vulnerability mitigation, such as cyber threat intelligence (CTI) sharing platforms, artificial intelligence, natural language processing (NLP), detection, and visualization models. It also discusses the use of risk assessment and management architectures, where semantic languages are used for threat information exchange, helping with dynamic risk assessment and improving vulnerability management.
In [
7], the authors present an innovative solution that combines advanced cryptography and blockchain technology to strengthen the security and reliability of smart grids, addressing existing vulnerabilities and preparing the infrastructure for a more decentralized and secure future. The solution utilizes a hybrid authentication and handshake algorithm (BSHAHA), which employs both symmetric and asymmetric cryptography. BSHAHA demonstrates itself to be a robust solution for authentication and data security in smart grids. The paper referenced [
8] emphasizes that there is no universally optimal solution for all security challenges in smart grids. Instead, it advocates for a holistic approach that integrates various technologies and methodologies. Among the prominent solutions discussed are blockchain, artificial intelligence (AI), and advanced encryption systems, which collectively enhance the security and resilience of smart grids.
The study presented in [
9] explores the detection of irregularities in smart meter data. These anomalies may arise from various sources, including information security breaches, data entry errors, and inconsistencies that affect the accuracy of readings. The authors propose leveraging machine learning techniques to analyze numerical data and identify unusual patterns. Additionally, they recommend examining historical data to detect significant deviations, aiming to minimize these issues and enhance the reliability of smart meters. Meanwhile, the research in [
10] discusses the relationship between anomie and smart grids, highlighting the challenges posed by the interoperability of physical and network systems. This integration exposes the infrastructure to cyber threats, such as false data injection (FDI) attacks, which can compromise the integrity of information processed by state estimation (SE) systems. To mitigate these vulnerabilities, the study suggests employing advanced intrusion detection and localization techniques, including Convolutional Neural Networks (CNNs) and bad data detectors (BDDs).
In [
11], the authors propose a CTI model designed to strengthen the security of an organization’s core processes and strategies through technologies such as artificial intelligence, blockchain, and multi-factor authentication. The primary objective is to enhance organizational resilience against cyber threats and promote continuous information sharing to improve security. In [
12], the authors look at smart grid vulnerabilities and recommend using technological solutions to reduce risks. The use of tree-based algorithms, which greatly improve attack detection and allow for more efficient responses, is emphasized in the study. It also suggests implementing strong authentication and encryption procedures to guard against illegal access and data alteration and maintain the confidentiality and integrity of information.
To detect cyberattacks targeting energy theft in renewable energy systems, in [
13], the authors utilized deep learning techniques, integrating data from smart meters, weather predictions, and SCADA systems. This methodology encourages smart meter adoption, leading to enhanced accuracy in energy consumption reporting. Their mitigation strategy, employing deep neural networks like recurrent and convolutional networks, achieved a high detection rate of 99.3% with a low false negative rate of 0.22%. In [
14], the authors presented a review of security and privacy risks, exploring both traditional and machine learning-driven (supervised and unsupervised) countermeasures to identify anomalous patterns indicative of threats.
In [
15], the authors proposed a multilayer security framework designed to protect against attacks, ensure data integrity, and prevent energy fraud. Their system leverages LoRaWAN, a technology well suited for IoT applications that prioritize energy efficiency and wide-area coverage due to its long range and low power consumption.
Security is significantly enhanced with the use of AES encryption, an advanced standard that protects information against unauthorized access. In addition, we have implemented unidirectional data transmission, a strategy that makes data interception and manipulation difficult, as the flow of information occurs in only one direction, making it more complex for an attacker to insert themselves into the communication. The system is also designed to mitigate distributed denial-of-service (DDoS) attacks, which aim to destabilize the network by overloading devices with excessive traffic.
3. Research Methodology
This research adhered to the guidelines outlined in [
16] for conducting a systematic mapping study (SMS). The decision to use this method was motivated by a number of factors. It offers a structured and methodical approach to identifying, evaluating, and interpreting all pertinent studies pertaining to a specific research question, focus area, or phenomenon of interest. An SMS is a clearly defined and methodical method for reviewing and analyzing empirical evidence related to a particular method or technique, pinpointing existing research gaps and areas, and supplying the foundational knowledge necessary to guide future research endeavors for scholars or practitioners. In contrast to traditional literature reviews, systematic mapping studies demand more time and effort; however, they yield a more profound comprehension of the subject matter and a more robust groundwork for formulating research inquiries [
17]. A standard systematic mapping research protocol typically involves the following five distinct stages:
- 1.
Formulation of research questions;
- 2.
Definition of the search process and search string;
- 3.
Definition of the study selection process, including inclusion and exclusion criteria;
- 4.
Extracting data and mapping data to specific research questions;
- 5.
Data analysis and results extraction.
3.1. Definition of Research Questions
The first step in conducting an SMS is defining the research questions. This process is crucial for ensuring the appropriate selection of relevant articles. The aim of this study is to explore the cyber threats, vulnerabilities, and cybersecurity measures related to smart meter data within smart grids. With these objectives in mind, the following research questions were formulated to guide the study:
QP1: What are the main cyber threats to smart meter data in smart grids?
QP2: What vulnerabilities in smart meter infrastructure can be exploited by cyberattacks?
QP3: What are the common strategies and technologies used to mitigate cybersecurity risks in smart grids?
QP4: What are the current research gaps in cybersecurity for smart meter data?
The first research question is to begin collecting information from the newspaper about the types of cybersecurity threats in order to determine which one is the most threatening in response to QP1. Subsequently, the second research question focuses on vulnerabilities within smart meter infrastructure that can be exploited by cyberattacks, aiming to identify the most susceptible areas to threats. The third research question evaluates the effectiveness of current cybersecurity measures and protocols in protecting smart meter data. This analysis aims to highlight practices that are working well and identify areas for improvement. The fourth research question aims to identify gaps in current research and suggest areas for future research, thereby providing further directions for research in this rapidly developing field. Finally, the fifth research question offers recommendations to enhance cybersecurity for smart meter data in smart grid networks, ensuring robust protection against emerging threats.
3.2. Search Protocol and Selection
To conduct an effective systematic review, it is essential to establish a robust search protocol and clear article selection criteria. This protocol needs to be carefully outlined to ensure the inclusion of pertinent and high-quality studies. In this study, the PICOC strategy (Population, Intervention, Comparison, Outcome, Context) will be adopted to guide the search and selection process, as detailed below:
Population: Smart meters used in smart grids;
Intervention: Cybersecurity measures and strategies implemented to protect data;
Comparison: Comparison of different cybersecurity strategies and their effectiveness;
Outcome: Identification of threats, vulnerabilities, effectiveness of security measures, and research gaps;
Context: Cybersecurity in the context of smart grids.
The research was performed in Scopus, Web of Science, and IEEE Xplore, chosen for their comprehensiveness and relevance in the fields of technology and cybersecurity. The search string is specific to ensure comprehensive and targeted article collection, as shown in
Table 1.
These search strategies were chosen to ensure the inclusion of relevant studies published over a 10-year period from 2013 to 2024. The article selection process will be conducted in several stages. Initially, a preliminary search will be conducted to identify all potentially relevant articles. Next, the titles and abstracts of these articles will be reviewed for initial screening, eliminating those that are clearly unrelated to the research questions. The remaining articles will be fully assessed for relevance and methodological quality. Inclusion and exclusion criteria will be applied rigorously as shown in
Table 2 and
Table 3. Inclusion criteria encompass articles published in peer-reviewed journals or recognized conferences. Opinion articles, non-peer-reviewed studies, and non-academic publications will be excluded.
Figure 1 presents the PRISMA 2020 flowchart, adapted from [
18]. This systematization is important so that the scientific document acquisition and selection procedure can benefit from reproducibility and, thus, promote methodological robustness for the process. The initial search resulted in a total of 2910 articles, distributed in the IEEE (1161 articles), Scopus (1352 articles), and Web of Science (397 articles) repositories. This resulted in a total of 1400 duplicates and false positives that were removed before the screening process, as shown in
Table 4.
Next, specific inclusion and exclusion criteria were applied, as illustrated in
Figure 1, reducing the set to 41 selected articles, described in
Table 5. This search reflects the necessary comprehensiveness to capture a full spectrum of research related to the topic. However, the application of rigorous inclusion and exclusion criteria was essential to ensure the relevance and quality of the studies considered in the final analysis. The process of reducing the number of articles followed a structured protocol, which included screening titles and abstracts, followed by a full reading of the remaining articles.
To facilitate this systematic review, we employed the Parsifal v2.2 software [
19], which enabled a more efficient and reproducible selection process.
Table 5.
Articles selected through systematic mapping of literature.
Table 5.
Articles selected through systematic mapping of literature.
Title | Ref | Year |
---|
A Review of Anomaly Detection Techniques in Advanced Metering Infrastructure | [20] | 2020 |
A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence | [21] | 2024 |
A Comprehensive Review on Cyber-Attacks in Power Systems: Impact Analysis, Detection, and Cyber Security | [22] | 2024 |
A Deep Learning Framework to Identify Remedial Action Schemes against False Data Injection Cyberattacks Targeting Smart Power Systems | [23] | 2024 |
A Novel Approach for Detection of Cyber Attacks in Microgrid SCADA System | [24] | 2023 |
A Novel False Data Method Targeting on Time-Series in Smart Grid | [25] | 2023 |
A Review of Cyber-Resilient Smart Grid | [26] | 2022 |
A Review of Features, Vulnerabilities, Cyber-Attacks and Protective Actions in Smart Grid Systems | [27] | 2023 |
A Review of Various Modern Strategies for Mitigation of Cyber Attacks in Smart Grids | [28] | 2019 |
A Review on Cyber Security Issues and Mitigation Methods in Smart Grid Systems | [29] | 2017 |
A Survey on Smart Grid Metering Infrastructures: Threats and Solutions | [30] | 2015 |
Analyzing Attack Resilience of an Advanced Meter Infrastructure Reference Model | [31] | 2016 |
Anomaly Detection in Smart Meters: Analytical Study | [9] | 2022 |
Attacks, Vulnerabilities and Security Requirements in Smart Metering Networks | [32] | 2015 |
Cyber Security Vulnerabilities of Smart Metering Based on LPWAN Wireless Communication Technologies | [33] | 2020 |
Real-Time Detection of Cyber-Attacks in Modern Power Grids with Uncertainty Using Deep Learning | [34] | 2022 |
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges | [35] | 2018 |
Security Aspects in Smart Meters: Analysis and Prevention | [36] | 2020 |
Smart Meter Vulnerability Assessment under Cyberattack Events—An Attempt to Safeguard | [37] | 2023 |
Smart Meter Security: Vulnerabilities, Threat Impacts, and Countermeasures | [38] | 2019 |
Using Smart Meter Data to Predict and Identify Consumer Vulnerability | [39] | 2023 |
Cyber-Physical Vulnerability Assessment in Smart Grids Based on Multilayer Complex Networks | [40] | 2021 |
Invasion Analysis of Smart Meter in AMI System | [41] | 2021 |
Smart Meter Data Privacy: A Survey | [3] | 2017 |
Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism | [42] | 2017 |
Simulation of SCADA System for Advanced Metering Infrastructure in Smart Grid | [43] | 2020 |
Intrusion Detection Tool for Residential Consumers Equipped with Smart Meters | [44] | 2023 |
Smart Meters: Cyber Security Issues and Their Solutions | [45] | 2023 |
Review of Cybersecurity Analysis in Smart Distribution Systems and Future Directions for Using Unsupervised Learning Methods for Cyber Detection | [46] | 2023 |
Intrusion Detection System for Smart Meters | [47] | 2020 |
Semi Supervised Cyber Attack Detection System for Smart Grid | [48] | 2022 |
Security and Privacy Challenges, Solutions, and Open Issues in Smart Metering: A Review | [49] | 2021 |
Securing the Smart Grid: A Comprehensive Analysis of Recent Cyber Attacks | [50] | 2024 |
Smart Meter Data Analytics for Load Prediction Using Extreme Learning Machines and Artificial Neural Networks | [51] | 2019 |
Cyber Security Enhancement of Smart Grids via Machine Learning—A Review | [52] | 2020 |
Cybersecurity Threats, Detection Methods, and Prevention Strategies in Smart Grid: Review | [5] | 2023 |
Smart Meter Modbus RS-485 Intrusion Detection by Federated Learning Approach | [53] | 2023 |
Non-Intrusive Load Monitoring Based Demand Prediction for Smart Meter Attack Detection | [54] | 2021 |
Securing Smart Grid: Cyber Attacks, Countermeasures, and Challenges | [55] | 2012 |
Smart Grid Security and Privacy: From Conventional to Machine Learning Issues (Threats and Countermeasures) | [14] | 2022 |
During this initial screening, many articles were excluded (rejected) for not directly addressing the research questions or for presenting redundant studies or studies of lower methodological quality. Additionally, it was crucial to conduct a qualitative assessment of the selected articles to ensure that only relevant studies were retained and contributed to the objectives of the SMS. Our objective scoring system ensured a fair and transparent selection process, resulting in 41 articles representing the most relevant and methodologically sound research. This rigorous approach reduced the number of studies and strengthened the foundation of the conclusions.
To ensure the quality and reliability of the selected articles, we conducted a rigorous quality assessment process. This assessment focused on the relevance of the work to the research field. We asked ourselves the following questions:
Does this article significantly contribute to advancing knowledge in the area of study?
Publication in a relevant journal or conference: Was the article published in a prestigious journal or conference recognized in the field of study?
These questions helped ensure that only the most relevant and reliable articles were considered in the final analysis.
3.3. Bias Risk Assessment
To assess the reliability of the included studies, a risk of bias evaluation was conducted using Parsifal v2.2, following the PRISMA 2020 guidelines. This process enabled a structured and transparent selection of the analyzed scientific articles. The risk of bias assessment considered four main categories:
Selection bias: Assesses whether the inclusion and exclusion criteria were applied in a clear and objective manner;
Information bias: Refers to the methodological quality of the studies and the reliability of the extracted data;
Publication bias: Identifies potential gaps due to the non-publication of studies with negative or inconclusive results;
Reporting bias: Examines whether the studies reported all data in a complete and transparent manner.
The studies were classified into three categories:
Low risk of bias: Robust and well-described methodology, with reliable data;
Moderate risk of bias: Some methodological limitations, but with still usable data;
High risk of bias: Significant methodological flaws, potentially compromising the results.
Although rigorous criteria were applied to minimize bias in the selection and analysis of the studies, some limitations inherent in the systematic review process were considered, as follows:
Publication bias: It is possible that studies with negative or neutral results were not included, as these studies are less frequently published. To mitigate this bias, widely recognized databases (Scopus, Web of Science, and IEEE Xplore) were used to capture the broadest spectrum of possible publications.
Selection bias: The exclusion of studies that did not meet the established methodological criteria may have limited the diversity of the analyzed approaches. However, this filtering was necessary to ensure the quality and reliability of the extracted data.
Information bias: Some studies included in the review did not present sufficient methodological details, which may have impacted the accuracy of data extraction. To minimize this problem, the data were analyzed by more than one reviewer, ensuring a consensus in the interpretation of the findings.
Methodological bias: The inclusion of studies published in the last 10 years may have excluded older references, but this decision was based on the need to capture the most current trends in cybersecurity for smart meters.
Despite these limitations, the methodological approach used, including the use of Parsifal v2.2 and adherence to PRISMA 2020, ensures the transparency and reproducibility of the review process. Future studies may expand this analysis by including gray literature, pre-prints, and quantitative meta-analyses to strengthen the evidence on cybersecurity risks in smart meters.
3.4. Data Extraction
After selecting 41 papers included in this study, data extraction was conducted to answer the original research questions. The main information extracted is as follows:
Study title;
Authors;
Publication year;
Objectives;
Methodology;
Key findings;
Cybersecurity threats identified;
Security measures discussed;
Effectiveness of measures;
Identified gaps;
Recommendations.
Each category of information was carefully documented to allow for a detailed analysis and comprehensive synthesis of the data. This meticulous process ensures that all research questions are addressed thoroughly and accurately, providing a solid foundation for the conclusions and recommendations of this study. The selected articles are placed in
Table 5, where we can see their names and publication years.
5. Discussion
The results and answers to the research questions in
Section 3 are discussed below.
QP1: What are the main cyber threats to smart meter data in smart electrical grids?
The main cybersecurity threats to smart meter data in smart grids, as presented in
Table 6, were identified based on a systematic review of several articles. These threats can significantly compromise the security and efficiency of these networks. Among the most highlighted threats in the selected articles, four stand out: data tampering/manipulation with 91.67, unauthorized access with 75% frequency in articles, privacy leaks with 58.33%, and denial-of-service (DoS)/distributed denial-of-Service (DDoS) with 50.00%. These threats were selected from several discussed in the articles reviewed, standing out for their potential impact and the frequency with which they were mentioned in the literature. The systematic analysis reinforces the importance of mitigating these threats to ensure the security and efficiency of smart grids.
QP2: What vulnerabilities in smart meter infrastructure can be exploited by cyberattacks?
Based on the frequency of occurrence identified in the reviewed articles, and as shown in
Table 6 and
Table 7 analyzing the frequency data, it is clear that vulnerabilities in smart meter infrastructure represent serious security risks in cybernetics. The high incidence of issues related to weak encryption, poor authentication, insecure communication protocols, and inadequate data protection reflects the widespread vulnerability of these systems. These issues are discussed in multiple studies due to their critical importance for the integrity and security of data in smart meters. Therefore, mitigating these vulnerabilities is essential to protect these systems against potential cyberattacks and ensure the reliability of smart grids.
QP3: What are the common strategies and technologies used to mitigate cybersecurity risks in smart grids?
To answer this question about the common strategies and technologies used to mitigate cybersecurity risks in smart electrical grids, consider the data provided by the selected articles summarized in
Table 8 and
Table 9. The common strategies include using advanced deep learning techniques (such as Convolutional Neural Networks and Autoencoders) and machine learning (such as Support Vector Machines and Random Forest) for anomaly detection and the prediction of anomalous behavior.
Encryption is crucial for protecting sensitive data, and vulnerability analysis, anomaly detection, and thorough security testing are essential methods for identifying and addressing weaknesses before they are exploited by cyberattacks. For example, one of the major vulnerabilities in smart grids is false data injection (FDIA), as discussed in [
22]. This type of attack can severely compromise the integrity and operation of energy systems. To mitigate this threat, we can implement the deep learning methods described in [
23,
42]. In this study, we propose creating a model that learns common data patterns and detects anomalies that indicate attacks, which can identify and mitigate FDIA attacks in real time. Another important vulnerability is the vulnerability of the power grid to denial-of-service (DoS) attacks, as detailed in [
26]. These attacks can disrupt the control and communication systems. To address this issue, the authors in [
28] propose network segmentation and defense-in-depth techniques to limit the impact of such attacks, while the authors in [
50] propose the use of redundant networks and the implementation of a fast recovery system to improve resilience against DoS attacks, minimizing downtime and damage. Furthermore, the manipulation of metering data from smart meters is a significant vulnerability, as discussed in [
36]. To mitigate this vulnerability, the authors in [
20] propose the implementation of machine learning and statistical anomaly detection methods that can identify anomalous patterns in measurement data. Complementing this approach, the authors in [
44] propose developing a dedicated intrusion detection tool for smart meters that monitors data traffic and detects manipulation attempts in real time. Some mitigation solutions have been proposed in the literature regarding intrusions and manipulations of SCADA systems in microgrids, as discussed in [
24]. For instance, the authors in [
34] suggest the evaluation of cyber and physical vulnerabilities through complex multilayer networks, identifying critical points that require additional protection to prevent intrusions. Similarly, the authors in [
48] propose the development of semi-supervised detection systems that combine supervised and unsupervised machine learning to identify intrusion attempts in SCADA systems of microgrids.
Finally, the exposure to privacy attacks in smart meters, as reviewed [
3], is a growing concern. To mitigate this vulnerability, the authors in [
35] suggest the implementation of data anonymization and aggregation techniques, which protect user privacy by preventing direct association between the collected data and specific individuals. Additionally, the authors in [
49] propose the use of advanced encryption and access control techniques to ensure that only authorized users can access measurement data, thereby protecting consumer privacy.
QP4: What gaps exist in current research on the cybersecurity of smart meter data?
Analyzing the previous questions and the data provided in
Figure 2 and
Figure 3 and
Table 8 (QP4) of the articles selected in the systematic mapping, we identified some gaps in current research on the cybersecurity of smart meter data, such as the following:
Efficiency and scalability: Many cybersecurity techniques are computationally intensive, which can limit their applicability to large smart meter networks. It is necessary to develop more efficient and scalable methods to deal with the growing volume of data generated by these systems. In [
21], the authors explore the efficiency and scalability of these approaches, crucial for dealing with the complexity and volume of data generated by smart grids. Furthermore, the article discusses the application of these techniques in real scenarios, highlighting their practical advantages and limitations [
21]. Real-time detection: The ability to detect real-time anomalies and intrusions in meter data is crucial to mitigating damage to the electrical grid.
As seen in [
22], this research highlights the relevance of critical security issues affecting modern electronic systems. A coordinated and adaptable approach is essential to safeguarding critical infrastructures against constantly evolving cyber threats. The study explores the use of technologies such as behavioral analysis and network monitoring, as well as the application of artificial intelligence, including neural networks and machine learning algorithms designed for anomaly detection. The system aims to identify suspicious patterns that may indicate a threat and implement measures to mitigate potential damage. As cyberattack techniques continue to advance, the need for effective prevention strategies becomes increasingly crucial.
The main focus is to develop effective methods of identifying corrective action schemes that can neutralize these attacks, allowing power systems to automatically implement corrective measures or alert operators for immediate human intervention.
These gaps indicate critical areas where additional research can significantly contribute to strengthening the cybersecurity of smart meters and ensuring the reliability of smart grids in the future.
6. Research Gaps and Proposals for Future Work
The analysis of the main cyber threats to smart meters highlighted several areas for attention, but also revealed significant gaps that still need to be addressed by the academic and industrial communities.
One of the most notable gaps is the lack of consensus on unified security standards for smart meters. Although there are several proposals for security protocols and cyberattack mitigation mechanisms, such as advanced encryption and anomaly detection [
23,
24], these mechanisms have not yet been widely and uniformly implemented in the industry. The development of internationally recognized security standards specifically aimed at protecting smart meters would be an important contribution to increasing the security of smart grids. Future work could explore the creation of such standards, collaborating with regulatory bodies and critical infrastructure industries.
Another gap identified is the lack of studies focused on the resilience of systems in large-scale attack scenarios. While the paper discusses the dangers of DDoS and malicious command injection attacks [
34], there were few studies investigating system recovery after successful attacks. Future work could focus on developing and testing recovery strategies that allow smart metering systems to quickly restructure after a security breach, minimizing the impact on consumers.
In addition, the paper presents a number of known attacks and vulnerabilities [
36], but does not delve deeply into emerging vulnerabilities associated with new technologies integrated into smart grids, such as edge computing and 5G. These new technologies offer benefits of reduced latency and increased connectivity, but they also introduce new attack vectors that need to be understood and mitigated. Future studies can examine how these technologies affect smart meters and propose solutions to protect networks that include these innovations.
Finally, it is important to note the lack of end-user-centric studies, especially with regard to awareness and education about the cyber risks associated with smart meters. While the main focus of the research is on the infrastructure and technical aspects of threat mitigation, future work could explore the impact of security awareness programs aimed at end users, helping them better understand how to protect their devices and data in smart grids. In summary, the following future work is suggested:
- 1.
Developing unified security standards for smart meters in collaboration with regulatory agencies;
- 2.
Studies on system resilience and recovery after large-scale cyberattacks;
- 3.
Investigating emerging vulnerabilities associated with technologies such as edge computing and 5G within the context of smart grids;
- 4.
Creating security awareness programs to educate end users on how to protect their data in smart meter networks.
These areas represent significant gaps in the field of smart meter cybersecurity, and by addressing them, future work could significantly contribute to the security and reliability of these networks.