Cross-layer security framework for smart grid: Physical security layer

Farag, Mohammed M.; Azab, Mohamed; Mokhtar, Bassem

doi:10.1109/isgteurope.2014.7028963

Cited by 22 publications

(10 citation statements)

References 12 publications

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“…While a great amount of research has been done towards these objectives, science and technology related to the cyber-physical security of SGs are still immature. Additionally, much of the critical infrastructure is currently transitioning towards the paradigm of SGs by increasing the dependency of control of physical processes on communication networks, thus becoming exposed to cyber-threats [1]. Guaranteeing the reliable operation of power grids is crucial for today's society and it is done through real-time power system monitoring.…”

Section: Introductionmentioning

confidence: 99%

Hybrid data‐driven physics model‐based framework for enhanced cyber‐physical smart grid security

et al. 2020

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This paper presents a hybrid data-driven physics model-based framework for real time monitoring in smart grids. As the power grid transitions to the use of smart grid technology, it's real time monitoring becomes more vulnerable to cyber attacks like false data injections (FDI). Although smart grids cyber-physical security has an extensive scope, this paper focuses on FDI attacks, which are modeled as bad data. State of the art strategies for FDI detection in real time monitoring rely on physics model-based weighted least squares state estimation solution and statistical tests. This strategy is inherently vulnerable by the linear approximation and the companion statistical modeling error, which means it can be exploited by a coordinated FDI attack. In order to enhance the robustness of FDI detection, this paper presents a framework which explores the use of data-driven anomaly detection methods in conjunction with physics model-based bad data detection via data fusion. Multiple anomaly detection methods working at both the system level and distributed local detection level are fused. The fusion takes into consideration the confidence of the various anomaly detection methods to provide the best overall detection results. Validation considers tests on the IEEE 118 bus system.

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Section: Introductionmentioning

confidence: 99%

Hybrid data‐driven physics model‐based framework for enhanced cyber‐physical smart grid security

et al. 2020

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“…Since the invention of PSSE, the problem of bad data detection has been a topic of great interest [5]. At first, GEs were considered to come only from faulty meters or communication errors, but the advent of SG has introduced the threat of cyber‐attacks and increased potential for system‐harming GEs [1–4]. The FDI attack in which a subset of measurement values is modified can have significant impacts on a power system if it goes undetected.…”

Section: Background Informationmentioning

confidence: 99%

“…These next‐generation power systems aim to achieve greater stability, efficiency and robustness of the physical processes on the grid through the integration of control, communication and computation. With the transition toward the SG paradigm and advanced technology implementations, there is an increasing vulnerability to cyber‐threats, especially among critical infrastructures [1]. Although the technologies themselves have advanced rapidly, research into the cyber‐physical security of SGs is still immature.…”

Section: Introductionmentioning

confidence: 99%

Ensemble CorrDet with adaptive statistics for bad data detection

et al. 2020

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“…Un ejemplo de seguridad en las SG se detalla en Farag et al (2014), en donde los resultados de la simulación inicial en un estudio sobre el marco de seguridad de capas cruzadas ilustran la factibilidad y efectividad de la capa de seguridad física desarrollada utilizando un modelo conceptual de capas de seguridad. Es decir, la asignación de múltiples barreas de acceso codificado para la posible intromisión en la información registrada por los medidores de energía.…”

Section: Seguridad En Sgunclassified

Visión General, Características y Funcionalidades de la Red Eléctrica Inteligente (Smart Grid)

2018

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Este artículo presenta una visión general de la red eléctrica inteligente (Smart Grid) con sus características y funcionalidades y se identifica las actividades de investigación, tendencias, problemas y desafíos de la Smart Grid, Para ello se realizó una revisión de la literatura donde se visualiza como la integración de los recursos energéticos distribuidos (generación distribuida y almacenamiento energético), así como los avances de las tecnologías de la información y las comunicaciones, han desarrollado el concepto de la moderna red eléctrica. En esta área, la implementación de políticas y la rápida integración de sistemas de gestión, están teniendo como resultado una gran revolución en la generación, la trasmisión, la distribución, la operación y el consumo energético.

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Cross-layer security framework for smart grid: Physical security layer

Cited by 22 publications

References 12 publications

Hybrid data‐driven physics model‐based framework for enhanced cyber‐physical smart grid security

Hybrid data‐driven physics model‐based framework for enhanced cyber‐physical smart grid security

Ensemble CorrDet with adaptive statistics for bad data detection

Visión General, Características y Funcionalidades de la Red Eléctrica Inteligente (Smart Grid)

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