Assessing Loss Event Frequencies of Smart Grid Cyber Threats: Encoding Flexibility into FAIR Using Bayesian Network Approach

Le, Anhtuan; Chen, Yue; Chai, Kok Keong; Vasenev, Alexandr; Montoya, Lorena

doi:10.1007/978-3-319-47729-9_5

Cited by 9 publications

(14 citation statements)

References 43 publications

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“…The developed method reduces computational burden over communication networks. In [47], a Bayesian network is introduced for obtaining quantitative loss event frequency results of high granularity using traceable and repeatable process. This proposed technique differentiates the most effective part of a certain threat that is useful for plan countermeasures in a better way.…”

Section: Recent Advances Of the Signal Processing Techniques In Smartmentioning

confidence: 99%

Recent advances of the signal processing techniques in future smart grids

Uddin

Ahmad

Qamar

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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IntroductionSmart grid is a network of electric supply that manages power demand in reliable and economic manner by detecting and reacting to local changes in usage. The infrastructure comprises of smart meters, appliances, and resources with a combination of modern technologies like, control, power, instrumentation, and communication. In such a complex scenario, signal processing techniques are essential to understand, plan, design and operate the complex future smart electronic grids [1]. In addition to this, signal processing has wide variety of applications and is becoming an important tool for electric power system analysis. This is due to the fact that measurements retrieved from numerous locations of the grid can be used for data analysis. These measurements can also be used for a variety of issues such as voltage control, power quality and reliability, power system and equipment diagnostics, power system control and protection, etc [2][3][4][5][6].Power quality is one of the main issue of the smart grid research where voltage, current and frequency deviations in the power system are the main concerns of the system operator [7]. The characterization of the incompatibilities caused by these deviations requires an understanding of their principal cause. Other possible aspects that need inspection are the efficient representation of the voltage and current variations in various electrical Abstract Smart grid is an emerging research field of the current decade. The distinguished features of the smart grid are monitoring capability with data integration, advanced analysis to support system control, enhanced power security and effective communication to meet the power demand. Efficient energy consumption and minimum costs are also included in the prodigious features of smart grid. The smart grid implementation requires intelligent interaction between the power generating and consuming devices that can be achieved by installing devices capable of processing data and communicating it to various parts of the grid. The efficiency of these devices is greatly dependent on the selection and implementation of the advance digital signal processing techniques. This paper provides a comprehensive survey on the applications of signal processing techniques in smart grids, plus the challenges and shortcomings of these techniques. Furthermore, this paper also outlines some future research directions related to applications of signal processing in smart grids. Uddin et al. Hum. Cent. Comput. Inf. Sci. (2018) Page 2 of 15 Uddin et al. Hum. Cent. Comput. Inf. Sci. (2018) 8:2 equipment. Moreover, the signal processing of the power patterns leads to better understanding the behavior of these equipment. Continuous monitoring is also required to capture various events and variations. To meet future demands, methods and techniques must be developed to explore the full range of signals derived from the complex interaction between suppliers, consumers and network operators [8]. REVIEWA smart grid performs measurement, monitoring ...

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Section: Recent Advances Of the Signal Processing Techniques In Smartmentioning

confidence: 99%

Recent advances of the signal processing techniques in future smart grids

Uddin

Ahmad

Qamar

et al. 2018

Hum. Cent. Comput. Inf. Sci.

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“…These tools have been already described in [5], [13], [12], [14]. We report a summary here for completeness.…”

Section: Tools Constituting the Modeling Frameworkmentioning

confidence: 99%

“…This tool allows numerical threat assessment based on the FAIR factors [12], namely Contact, Vulnerability, Action, and Control Strength. The numerical outputs given by BayesianFAIR can help to further rank threats in the same severity SE category (e.g.…”

Section: Bayesianfair Threat Evaluation (Bf Tool)mentioning

confidence: 99%

“…The numerical outputs given by BayesianFAIR can help to further rank threats in the same severity SE category (e.g. High or Very High), which is an extension of the FAIR framework [12]. This will be helpful to prioritize threats to assign the constraint security resources, especially in cases many threats are considered in the network [13].…”

Section: Bayesianfair Threat Evaluation (Bf Tool)mentioning

confidence: 99%

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A Modeling Framework to Support Resilient Evolution Planning of Smart Grids

Zoppi

Bessler

Ceccarelli

et al. 2017

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Cyber security is becoming more and more relevant with the advent of large-scale systems made of independent and autonomous constituent systems that interoperate to achieve complex goals. To ensure security of cyberphysical systems, it is important to analyze identified threats and their possible consequences. In case of smart grids as an example of a complex system, threats can result in power outages that damage the continuous supply of energy that is required from critical infrastructures. Therefore, city planners must take into account security requirements when organizing the power grid, including demand-side management techniques able to mitigate the adverse effects of outages, ultimately improving grid resilience. This paper presents a modeling framework developed within the IRENE project that brings together methodologies, policies and a toolset to evaluate and measure the resilience of the targeted smart grid. This will support stakeholders and city planners in their activities, specifically the resilient evolution planning of Smart Grids.Keywords: Threat Analysis · Smart Grids · Evolution · Resilience · City Planning · Power Flow Equations · Demand Side Management · IRENE Planning for ResilienceThis paper describes the modelling framework that was developed within the IRENE project [18]. This framework aggregates methodologies, policies and the toolset to evaluate the resilience of the targeted smart grid. The framework will be used to investigate threats in the smart grid and to implement the identified solutions. Based on the smart grid topology, possible outages and risk analyses, the framework provides a way to support city planners in their decisions. The usage of the modeling framework is then regulated by [19], which traces the bounds of the interaction among different users e.g., generic stakeholders, DNOs, city planners, regulators.

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“…(13) BayesianFAIR [10] is then used to calculate LEF of [Contact, Action, Threat Capability, Control Strength] of an individual threat. The vector elements are within the range of qualitative values from 'Very Low' to 'Very High'.…”

Section: An Example Of Applying the Threat Navigatormentioning

confidence: 99%

Threat Navigator: Grouping and Ranking Malicious External Threats to Current and Future Urban Smart Grids

Vasenev

Montoya

Ceccarelli

et al. 2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract.Deriving value judgements about threat rankings for large and entangled systems, such as those of urban smart grids, is a challenging task. Suitable approaches should account for multiple threat events posed by different classes of attackers who target system components. Given the complexity of the task, a suitable level of guidance for ranking more relevant and filtering out the less relevant threats is desirable. This requires a method able to distil the list of all possible threat events in a traceable and repeatable manner, given a set of assumptions about the attackers. The Threat Navigator proposed in this paper tackles this issue. Attacker profiles are described in terms of Focus (linked to Actor-to-Asset relations) and Capabilities (Threat-toThreat dependencies). The method is demonstrated on a sample urban Smart Grid. The ranked list of threat events obtained is useful for a risk analysis that ultimately aims at finding cost-effective mitigation strategies.

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Assessing Loss Event Frequencies of Smart Grid Cyber Threats: Encoding Flexibility into FAIR Using Bayesian Network Approach

Cited by 9 publications

References 43 publications

Recent advances of the signal processing techniques in future smart grids

Recent advances of the signal processing techniques in future smart grids

A Modeling Framework to Support Resilient Evolution Planning of Smart Grids

Threat Navigator: Grouping and Ranking Malicious External Threats to Current and Future Urban Smart Grids

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