Recommendations for smart grid security risk management

Lamba, Vikas; Šimková, Nikola; Rossi, Bruno

doi:10.1080/23335777.2019.1600035

Cited by 22 publications

(8 citation statements)

References 32 publications

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“…Smart grid utilities must implement different security mechanisms and techniques in each layer of the smart grid architecture. These techniques include anonymization, intrusion detection, data aggregation, authorization, authentication, trust management, access control and privileges (Wang et al, 2018b;Jain et al, 2016;Bari et al, 2014;Lamba et al, 2019). Researchers are encouraged to design and develop new security mechanisms especially for smart grid.…”

Section: Discussionmentioning

confidence: 99%

Smart grid reliability evaluation and assessment

Mashal¹

2022

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PurposeSmart grid is an integration between traditional electricity grid and communication systems and networks. Providing reliable services and functions is a critical challenge for the success and diffusion of smart grids that needs to be addressed. The purpose of this study is to determine the critical criteria that affect smart grid reliability from the perspective of users and investigate the role big data plays in smart grid reliability.Design/methodology/approachThis study presents a model to investigate and identify criteria that influence smart grid reliability from the perspective of users. The model consists of 12 sub-criteria covering big data management, communication system and system characteristics aspects. Multi-criteria decision-making approach is applied to analyze data and prioritize the criteria using the fuzzy analytic hierarchy process based on the triangular fuzzy numbers. Data was collected from 16 experts in the fields of smart grid and Internet of things.FindingsThe results show that the “Big Data Management” criterion has a significant impact on smart grid reliability followed by the “System Characteristics” criterion. The “Data Analytics” and the “Data Visualization” were ranked as the most influential sub-criteria on smart grid reliability. Moreover, sensitivity analysis has been applied to investigate the stability and robustness of results. The findings of this paper provide useful implications for academicians, engineers, policymakers and many other smart grid stakeholders.Originality/valueThe users are not expected to actively participate in smart grid and its services without understanding their perceptions on smart grid reliability. Very few works have studied smart grid reliability from the perspective of users. This study attempts to fill this considerable gap in literature by proposing a fuzzy model to prioritize smart grid reliability criteria.

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Section: Discussionmentioning

confidence: 99%

Smart grid reliability evaluation and assessment

Mashal¹

2022

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“…Based on the distribution and demand side management of SGs, as well as the classification of SG technologies (i.e., communication technology, information provision, computing intelligence, and cybersecurity in Figure 1), the related works on the applications of SGs using various techniques are classified in Table 1. Neural networks [10,11,21], smart meters [12][13][14]22], and artificial intelligence [15] were adopted for power control; cyberphysical systems [18,23,24], big data [16,19,25], machine learning [17,18,26], AI [20,27] were adopted for demand side management, and network security [28][29][30][31][32] was used for communication and information transmission. All of them were implemented in smart manufacturing applications.…”

Section: Smart Manufacturing Using Distribution and Demand Side Manag...mentioning

confidence: 99%

A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing

Hsu,

Jiang,

Lin

2023

Energies

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To enable highly automated manufacturing and net-zero carbon emissions, manufacturers have invested heavily in smart manufacturing. Sustainable and smart manufacturing involves improving the efficiency and environmental sustainability of various manufacturing operations such as resource allocation, data collecting and monitoring, and process control. Recently, a lot of artificial intelligence and optimization applications based on smart grid systems have improved the energy usage efficiency in various manufacturing operations. Therefore, this survey collects recent works on applications of artificial intelligence and optimization for smart grids in smart manufacturing and analyzes their features, requirements, and challenges. In addition, potential trends and further challenges for the integration of smart grids with renewable energies for smart manufacturing, applications of 5G and B5G (beyond 5G) technologies in the SG system, and next-generation smart manufacturing systems are discussed to provide references for further research.

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“…Cyber risk assessment methods for CPSs more often than not are domain specific, as they need to take into account safety as an impact factor additional to the “traditional” impact factors of confidentiality, integrity, and availability. For example, an overview of such methods specific to the smart grid case is provided in Reference [ 12 ]. A review of the traditional cybersecurity risk assessment methods that have been used in the maritime domain, is provided in Reference [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

Cyber Risk Propagation and Optimal Selection of Cybersecurity Controls for Complex Cyberphysical Systems

Kavallieratos

Σπαθούλας

Katsikas

2021

Sensors

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The increasingly witnessed integration of information technology with operational technology leads to the formation of Cyber-Physical Systems (CPSs) that intertwine physical and cyber components and connect to each other to form systems-of-systems. This interconnection enables the offering of functionality beyond the combined offering of each individual component, but at the same time increases the cyber risk of the overall system, as such risk propagates between and aggregates at component systems. The complexity of the resulting systems-of-systems in many cases leads to difficulty in analyzing cyber risk. Additionally, the selection of cybersecurity controls that will effectively and efficiently treat the cyber risk is commonly performed manually, or at best with limited automated decision support. In this work, we propose a method for analyzing risk propagation and aggregation in complex CPSs utilizing the results of risk assessments of their individual constituents. Additionally, we propose a method employing evolutionary programming for automating the selection of an optimal set of cybersecurity controls out of a list of available controls, that will minimize the residual risk and the cost associated with the implementation of these measures. We illustrate the workings of the proposed methods by applying them to the navigational systems of two variants of the Cyber-Enabled Ship (C-ES), namely the autonomous ship and the remotely controlled ship. The results are sets of cybersecurity controls applied to those components of the overall system that have been identified in previous studies as the most vulnerable ones; such controls minimize the residual risk, while also minimizing the cost of implementation.

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Recommendations for smart grid security risk management

Cited by 22 publications

References 32 publications

Smart grid reliability evaluation and assessment

Smart grid reliability evaluation and assessment

A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing

Cyber Risk Propagation and Optimal Selection of Cybersecurity Controls for Complex Cyberphysical Systems

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