Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models

Aziz, Saddam; Irshad, Muhammad; Haider, Sami Ahmed; Wu, Jianbin; Deng, Ding Nan; Ahmad, Sadiq

doi:10.3389/fenrg.2022.964305

Cited by 16 publications

(6 citation statements)

References 43 publications

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“…The application of ICT facilitates convenient and efficient interaction between different sectors in the smart grid [26]. However, this increased connectivity also exposes the smart grid's digitized assets to cyber threats [27].…”

Section: The Proposed Adversarial False Data Injection Attack Methodsmentioning

confidence: 99%

Adversarial false data injection attacks on deep learning‐based short‐term wind speed forecasting

Yang,

Liang,

Yang

et al. 2023

IET Renewable Power Gen

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Developing accurate wind speed forecasting methods is indispensable to integrating wind energy into smart grids. However, current state‐of‐the‐art wind speed forecasting methods are almost data‐driven deep learning models, which may incur potential adversarial cyberattacks. To this end, this paper proposes an adversarial false data injection attack tactic to investigate such a cyber threat. First, targeting the deep learning‐based short‐term wind speed forecasting model, an optimization model is constructed to obtain the optimally false data that should be injected into the forecasting model input so as to expand the prediction deviation as much as possible. Then, as the optimization model is non‐differentiable, a particle swarm optimization‐based method is developed to solve the optimization problem, in which the near‐optimal solution is able to be explored, directing the false data that should be injected. At last, numerical studies of the proposed attack tactic are conducted on different‐hour ahead wind speed forecasting models, revealing the feasibility and effectiveness.

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Section: The Proposed Adversarial False Data Injection Attack Methodsmentioning

confidence: 99%

Adversarial false data injection attacks on deep learning‐based short‐term wind speed forecasting

Yang,

Liang,

Yang

et al. 2023

IET Renewable Power Gen

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“…Unlike previous works, which focused primarily on binary classification solutions, the system in [121] addresses the issue of detecting FDI attacks as a problem of multi-class classification, with Convolutional Neural Network (CCN) serving as a multi-label predictor. In [122], a machine learning strategy was presented to detect and protect smart grids against False Data Injection Attacks (FDIA),, which merged feature selection and machine learning. The authors used supervised machine learning models to implement hybrid approaches and compared the suggested model in terms of accuracy, precision, recall, and F1 score.…”

Section: Research Backgroundmentioning

confidence: 99%

A Review on the Evaluation of Feature Selection Using Machine Learning for Cyber-Attack Detection in Smart Grid

Mohammed,

Al-Jumaily,

Singh

et al. 2024

IEEE Access

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The Smart Grid is a modern power grid that relies on advanced technologies to provide reliable and sustainable electricity. However, its integration with various communication technologies and IoT devices makes it vulnerable to cyber-attacks. Such attacks can lead to significant damage, economic losses, and public safety hazards. To ensure the security of the smart grid, increasingly strong security solutions are needed. This paper provides a comprehensive analysis of the vulnerabilities of the smart grid and the different approaches for detecting cyber-attacks. It examines the different vulnerabilities of the smart grid, including system vulnerabilities and cyber-attacks, and discusses the vulnerabilities of all its elements. The paper also investigates various approaches for detecting cyber-attacks, including rule-based, signature-based, anomaly detection, and ma-chine learning-based methods, with a focus on their effectiveness and related research. Finally, prospective cybersecurity approaches for the smart grid, such as AI approaches and blockchain, are discussed along with the challenges and future prospects of cyberattacks on the smart grid. The paper's findings can help policymakers and stakeholders make informed decisions about the security of the smart grid and develop effective strategies to protect it from cyber-attacks.

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“…The researchers have explored the use of AI algorithms to detect and predict cyber threats in smart grids. ML models are trained on historical data to identify patterns and anomalies that indicate potential attacks or malicious activities [70,71]. AI-based anomaly detection techniques are being developed to identify unusual behavior or deviations from normal operations in smart grids.…”

Section: Existing Researchmentioning

confidence: 99%

Cyberattacks in Smart Grids: Challenges and Solving the Multi-Criteria Decision-Making for Cybersecurity Options, Including Ones That Incorporate Artificial Intelligence, Using an Analytical Hierarchy Process

Bouramdane

2023

JCP

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Smart grids have emerged as a transformative technology in the power sector, enabling efficient energy management. However, the increased reliance on digital technologies also exposes smart grids to various cybersecurity threats and attacks. This article provides a comprehensive exploration of cyberattacks and cybersecurity in smart grids, focusing on critical components and applications. It examines various cyberattack types and their implications on smart grids, backed by real-world case studies and quantitative models. To select optimal cybersecurity options, the study proposes a multi-criteria decision-making (MCDM) approach using the analytical hierarchy process (AHP). Additionally, the integration of artificial intelligence (AI) techniques in smart-grid security is examined, highlighting the potential benefits and challenges. Overall, the findings suggest that “security effectiveness” holds the highest importance, followed by “cost-effectiveness”, “scalability”, and “Integration and compatibility”, while other criteria (i.e., “performance impact”, “manageability and usability”, “compliance and regulatory requirements”, “resilience and redundancy”, “vendor support and collaboration”, and “future readiness”) contribute to the evaluation but have relatively lower weights. Alternatives such as “access control and authentication” and “security information and event management” with high weighted sums are crucial for enhancing cybersecurity in smart grids, while alternatives such as “compliance and regulatory requirements” and “encryption” have lower weighted sums but still provide value in their respective criteria. We also find that “deep learning” emerges as the most effective AI technique for enhancing cybersecurity in smart grids, followed by “hybrid approaches”, “Bayesian networks”, “swarm intelligence”, and “machine learning”, while “fuzzy logic”, “natural language processing”, “expert systems”, and “genetic algorithms” exhibit lower effectiveness in addressing smart-grid cybersecurity. The article discusses the benefits and drawbacks of MCDM-AHP, proposes enhancements for its use in smart-grid cybersecurity, and suggests exploring alternative MCDM techniques for evaluating security options in smart grids. The approach aids decision-makers in the smart-grid field to make informed cybersecurity choices and optimize resource allocation.

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Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models

Cited by 16 publications

References 43 publications

Adversarial false data injection attacks on deep learning‐based short‐term wind speed forecasting

Adversarial false data injection attacks on deep learning‐based short‐term wind speed forecasting

A Review on the Evaluation of Feature Selection Using Machine Learning for Cyber-Attack Detection in Smart Grid

Cyberattacks in Smart Grids: Challenges and Solving the Multi-Criteria Decision-Making for Cybersecurity Options, Including Ones That Incorporate Artificial Intelligence, Using an Analytical Hierarchy Process

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