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Year-wise publications with the search string “smart grid” AND “cyber threats” OR “cyberattacks” OR “vulnerabilities” on Google Scholar.
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Smart Grids (SGs) are governed by advanced computing, control technologies, and networking infrastructure. However, compromised cybersecurity of the smart grid not only affects the security of existing energy systems but also directly impacts national security. The increasing number of cyberattacks against the smart grid urgently necessitates more...
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Citations
... While all three are essential, availability is the most critical aspect of the triad, as even brief periods of downtime can have severe consequences (Krause et al., 2021). Cyberattacks on SGs exploit vulnerabilities in systems, infrastructure and human behaviour (Ding, 2022). These attacks are diverse and can often be categorised based on their intent to compromise one or more aspects of the CIA triad. ...
The integration of information and communication technology into traditional power grids, transitioning them into smart grids, represents a significant step towards improved efficiency, reliability and sustainability. However, as these systems become more digitally dependent, they also become increasingly susceptible to cyberthreats, posing serious risks to national security, economic stability and public safety. Smart grids as a core component of critical energy infrastructure do not operate in isolation but are interconnected with other critical infrastructures such as water, telecommunications, transportation systems, etc. This interdependence increases the risk of cyberattacks, whereby disruptions in one sector can affect others, leading to widespread consequences. Thus, ensuring security is of the utmost importance. Cyberattacks on smart grids are no longer a theoretical concept. Rather, the question is, how prepared are countries to defend their smart grids from these sophisticated and rapidly evolving global threats? This paper explores the global issue of cybersecurity in smart grids, as it affects both developed and developing countries but with a particular focus on developing countries like India, where rapid digitisation combined with limited resources and expertise creates a unique set of challenges. By assessing India's current cyber security maturity level, this paper identifies key technical, operational and policy gaps that need to be addressed. Additionally, this paper highlights the potential of artificial intelligence and digital twin technology to significantly enhance the cybersecurity of smart grids, making them more resilient to emerging threats.
... The review in Ding et al. (2022) addresses cyber threats in smart grids by analyzing hardware, software, and data communication vulnerabilities. It categorizes attacks and highlights potential solutions, particularly blockchain and AI techniques. ...
... Demand response (DR) is a key technology that helps manage electricity demand by requesting consumers to reduce consumption during peak periods. However, it also introduces risks, as attackers can inject false signals into the DR system, potentially destabilizing the electricity grid (Gunduz and Das, 2020;Ding et al., 2022). DERs, such as solar and wind-based energy sources, diversify non-centralized energy generation and strengthen the grid. ...
... EMS and DMS are essential for controlling transmission and distribution networks, managing energy flow, identifying faults, and minimizing energy wastage. Attacks on these systems could lead to resource mismanagement, power outages, and system destabilization (Ding et al., 2022;Tatipatri and Arun, 2024). ...
Smart grids are modernizing the future of providing energy for everyone, allowing us to increase the efficiency of power generation, transmission, or distribution using information and communication technologies. However, the network structure of smart grids makes them vulnerable to varying levels of cyber threats. This paper provides a broad overview of cyber threats against smart grids, considering attack surfaces, communication network layers, and the core security triad of confidentiality, integrity, and availability. This survey also outlines emerging threats and covers current protection, prevention, detection, mitigation, and recovery measures, focusing on emerging technologies such as artificial intelligence and large language models (LLMs) in smart grid security. We analyze and show how previous work has tackled and approached similar themes in this area. Amongst our contributions are categorizing the critical parts of smart grids that are most vulnerable to attack, several threat taxonomies, and a review of the increasing importance of LLMs for enhancing grid security. This evaluation underscores the need for effective and robust security technologies to avoid the compromises that result from more sophisticated cyber attacks.
... Furthermore, the taxonomies might focus on cybersecurity in specific industries, such as Power Grid [38][39][40], the maritime sector [41] or the oil and gas sector [42], while others revolve around cybercrime and cyber-harm [12,[43][44][45][46][47], even a Covid-19-related cybersecurity attacks taxonomy [48] was created. The diversity in the established scopes and methodologies that are employed in the creation of the taxonomies, contributes to their nonhomogeneity. ...
The aim of this work is to provide a systematic literature review of techniques for taxonomy generation across the cybersecurity domain. Cybersecurity taxonomies can be classified into manual and dynamic, each one of which focuses on different characteristics and tails different goals. Under this premise, we investigate the current state of the art in both categories with respect to their characteristics, applications and methods. To this end, we perform a systematic literature review in accordance with an extensive analysis of the tremendous need for dynamic taxonomies in the cybersecurity landscape. This analysis provides key insights into the advantages and limitations of both techniques, and it discusses the datasets which are most commonly used to generate cybersecurity taxonomies.
... Due to the heavy dependence of innovative grid systems on computer networks, cyberattacks can disrupt the regular operation of power supply systems. This could result in significant losses in production and daily life, impacting industries such as agriculture and healthcare (Ding et al., 2022). The close interconnection between intelligent grid networks and computer systems makes them particularly vulnerable to cyberattacks by terrorists. ...
... Cyberattacks that exploit the vulnerabilities within intelligent grid networks, such as ransomware and malware, can cause significant damage to the power system (Ding et al., 2022). Once infiltrated, malware can rapidly spread, encrypt vital data, and turn off control systems. ...
This paper delves into the phenomenon of "kufal symbiosis,” which refers to the collaboration between artificial intelligence (AI) and terrorist organizations. It highlights AI’s potential to enhance the efficiency of terrorist operations. While AI can be used to recruit new members and plan more sophisticated attacks, security agencies face challenges in adapting this technology to counter terrorism effectively. This paper discussed the crucial need to strike a balance between individual privacy and national security, as well as the difficulties of managing large-scale data with limited resources. Additionally, the use of technology such as deepfakes and botnets by terrorist organizations might lead to confusion and intensify the impact of their attacks. The discussion also addresses cyberattacks on smart cities, exposing the vulnerabilities in infrastructure to cyber threats. In conclusion, while AI enhances the efficiency of terrorist operations, it also equips security agencies to prevent such threats despite the ongoing struggle to balance privacy and security.
... Future studies should focus on developing standards that govern the deployment of LLMs and ensure that they adhere to principles of fairness, accountability, and transparency. This includes addressing issues related to data privacy and the potential for bias amplification, which can undermine public trust in AI systems [75]. ...
The rapid proliferation of Large Language Models (LLMs) across industries such as healthcare, finance, and legal services has revolutionized modern applications. However, their increasing adoption exposes critical vulnerabilities, particularly through adversarial prompt attacks that compromise LLM security. These prompt-based attacks exploit weaknesses in LLMs to manipulate outputs, leading to breaches of confidentiality, corruption of integrity, and disruption of availability. Despite their significance, existing research lacks a comprehensive framework to systematically understand and mitigate these threats. This paper addresses this gap by introducing a taxonomy of prompt attacks based on the Confidentiality, Integrity, and Availability (CIA) triad, an important cornerstone of cybersecurity. This structured taxonomy lays the foundation for a unique framework of prompt security engineering, which is essential for identifying risks, understanding their mechanisms, and devising targeted security protocols. By bridging this critical knowledge gap, the present study provides actionable insights that can enhance the resilience of LLM to ensure their secure deployment in high-stakes and real-world environments.
... The fact that the attackers targeted crucial facilities inside the grids suggests that the assaults may be more damaging than was first anticipated. A more accurate identification of the likelihood of being susceptible, as well as the implementation of preventative actions and the detection of anomalies, may be accomplished by the use of Machine Learning (ML) strategies and algorithms for [6]. It is possible to consider these methods to be more advantageous due to the fact that they are able to access a greater quantity of data simultaneously, in addition to data obtained from a variety of devices, including smart meters, sensors, and control systems. ...
... These data sets are based on the various communication protocols described above and the sorts of cyber-attacks that they might potentially affect. CIC Modbus, DNP3 Intrusion detection, ICS dataset for smart grid anomaly detection, and Electra are examples of the sorts of data sets that were discussed in the previous paragraph [6]. During the course of our investigation, we concentrated on the Electra type data set. ...
The rise of renewable energy integration in smart grids brings new cybersecurity challenges, prompting this study to examine vulnerabilities in Smart Cyber-Physical Power Systems (CPPS). The integration of renewable energy sources, such as wind and solar, into smart grids poses operational risks due to their decentralized and variable characteristics, particularly within the communication layers essential for real-time monitoring and control. While increasing integration of renewable energy sources does not directly impact cybersecurity vulnerabilities, the primary challenge arises from their decentralization. Addressing this decentralization requires the use of cyber layers between supply and demand, introducing vulnerabilities of cyber threats to the control and communication systems of the power system. These layers, vulnerable to diverse cyber-attacks like false data injection (FDI), denial of service (DoS), and replay assaults, might compromise grid stability and security. To address these risks, the research proposes a hybrid approach that integrates conventional cybersecurity strategies with machine learning (ML) approaches to improve cyber-attack detection. The research highlights the use of deep learning models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, for real-time anomaly identification in grid data. These models, developed using a PSCAD-simulated dataset augmented with synthetic cyber-attacks, exhibit considerable advancements in threat identification and mitigation. The study emphasizes the difficulties in identifying cyber risks in grids with significant renewable integration, such as frequency instability and diminished system inertia, and suggests energy storage alternatives and sophisticated forecasting models to mitigate these issues. By incorporating a novel pre-processing method that leverages feature derivatives, the proposed models achieve over 98% accuracy in detecting cyber threats, providing a robust framework for protecting smart power grids from evolving cyber risks.
... The availability of the top services to smart grids implies that there is a possibility of a denial-of-service assault FIGURE 6: Papers sorted by year found on Google Scholar using the search terms "smart grid" AND "cyber threats" OR "cyberattacks" OR "vulnerabilities." [100]. ...
... By keeping an eye on network traffic, the attacker can obtain sensitive data. This danger will be faced by the Smart Grid [100] because to its extensive network, which consists of numerous network nodes and is difficult to maintain for the devices linked to the main network. The main worry in protecting data worldwide is that the Smart Grid presents the highest danger of data theft [100]. ...
... This danger will be faced by the Smart Grid [100] because to its extensive network, which consists of numerous network nodes and is difficult to maintain for the devices linked to the main network. The main worry in protecting data worldwide is that the Smart Grid presents the highest danger of data theft [100]. ...
Energy management decreases energy expenditures and consumption while simultaneously increasing energy efficiency, reducing carbon emissions, and enhancing operational performance. Smart grids are a type of sophisticated energy infrastructure that increase the generation and distribution of electricity's sustainability, dependability, and efficiency by utilizing digital communication technologies. They combine a number of cutting-edge techniques and technology to improve energy resource management. A large amount of research study on the topic of smart grids for energy management has been completed in the last several years. The authors of the present study want to cover a number of topics, including smart grid benefits and components, technical developments, integrating renewable energy sources, using artificial intelligence and data analytics, cybersecurity, and privacy. Smart Grids for Energy Management are an innovative field of study aiming at tackling various difficulties and magnifying the efficiency, dependability, and sustainability of energy systems, including: 1) Renewable sources of power like solar and wind are intermittent and unpredictable 2) Defending smart grid system from various cyber-attacks 3) Incorporating an increasing number of electric vehicles into the system of power grid without overwhelming it. Additionally, it is proposed to use AI and data analytics for better performance on the grid, reliability, and energy management. It also looks into how AI and data analytics can be used to optimize grid performance, enhance reliability, and improve energy management. The authors will explore these significant challenges and ongoing research. Lastly, significant issues in this field are noted, and recommendations for further work are provided.
... FIGURE 6: Papers sorted by year found on Google Scholar using the search terms "smart grid" AND "cyber threats" OR "cyberattacks" OR "vulnerabilities." [100]. ...
... By keeping an eye on network traffic, the attacker can obtain sensitive data. This danger will be faced by the Smart Grid [100] because to its extensive network, which consists of numerous network nodes and is difficult to maintain for the devices linked to the main network. The main worry in protecting data worldwide is that the Smart Grid presents the highest danger of data theft [100]. ...
... This danger will be faced by the Smart Grid [100] because to its extensive network, which consists of numerous network nodes and is difficult to maintain for the devices linked to the main network. The main worry in protecting data worldwide is that the Smart Grid presents the highest danger of data theft [100]. ...
Energy management decreases energy expenditures and consumption while simultaneously increasing energy efficiency, reducing carbon emissions, and enhancing operational performance. Smart grids are a type of sophisticated energy infrastructure that increase the generation and distribution of electricity’s sustainability, dependability, and efficiency by utilizing digital communication technologies. They combine a number of cutting-edge techniques and technology to improve energy resource management. A large amount of research study on the topic of smart grids for energy management has been completed in the last several years. The authors of the present study want to cover a number of topics, including smart grid benefits and components, technical developments, integrating renewable energy sources, using artificial intelligence and data analytics, cybersecurity, and privacy. Smart Grids for Energy Management are an innovative field of study aiming at tackling various difficulties and magnifying the efficiency, dependability, and sustainability of energy systems, including: 1) Renewable sources of power like solar and wind are intermittent and unpredictable 2) Defending smart grid system from various cyber-attacks 3) Incorporating an increasing number of electric vehicles into the system of power grid without overwhelming it. Additionally, it is proposed to use AI and data analytics for better performance on the grid, reliability, and energy management. It also looks into how AI and data analytics can be used to optimize grid performance, enhance reliability, and improve energy management. The authors will explore these significant challenges and ongoing research. Lastly, significant issues in this field are noted, and recommendations for further work are provided.
... These grids comprise various essential components, including smart meters, sensors, communication networks, and advanced control systems. Smart meters facilitate real-time monitoring of energy consumption, providing consumers with detailed insights into their usage patterns [4]. Sensors deployed across the grid collect data on critical parameters such as voltage, current, and frequency, enabling utilities to monitor grid health and performance continuously. ...
This comprehensive survey explores the critical role of cybersecurity in IoT-based smart grids, which integrate advanced technologies for enhanced energy management and efficiency. As these systems become increasingly interconnected, they are exposed to various cybersecurity threats, including malware, denial of service attacks, and data breaches. This paper examines the existing frameworks and standards for cybersecurity in smart grids, highlighting their strengths and limitations. We also discuss emerging technologies such as blockchain, machine learning, and secure communication protocols that offer innovative solutions for safeguarding these infrastructures. Through a systematic literature review and case study analysis, this survey identifies key challenges and future research directions, emphasizing the necessity of robust cybersecurity measures to ensure the reliability and security of smart grid systems.
... Each attack is not only significant in terms of the damage caused but also reveals critical vulnerabilities across interconnected infrastructure systems, making them relevant to global critical industries, including power grids, industrial control systems (ICSs), and SCADA systems. These attacks are not isolated incidents, but rather, they reflect broader security weaknesses that could lead to severe consequences across various industries [26]. ...
This study examines the cybersecurity vulnerabilities of energy IT infrastructure in the context of rapid digital transformation and network expansion. While these advancements have improved efficiency, they have also increased susceptibility to cyber threats, particularly from malware such as Industroyer, Triton, NotPetya, and BlackEnergy3. By analyzing these malware threats, this research identifies key weaknesses in energy systems and highlights the need for enhanced anomaly detection, network segmentation, and system integrity checks. To address these vulnerabilities, this study proposes a layered security architecture in which each layer—physical, control, network, application, and security—provides independent protection while integrating into a unified defense strategy. This research emphasizes the necessity of combining technical-, organizational-, and policy-based responses to bolster cybersecurity across all levels of energy infrastructure. Key recommendations include immediate vulnerability patching, improved international cooperation, stronger legal frameworks, enhanced cybersecurity education, and the establishment of a unified command structure. This study provides practical insights and strategies for strengthening the cybersecurity of energy systems through a comprehensive, integrated approach.
