Cyber-Physical Observability for the Electric Grid

Jacobs, Nicholas; Hossain-McKenzie, Shamina; Summers, Adam; Jones, C. Birk; Wright, Brian J.; Chavez, Adrian

doi:10.1109/tpec48276.2020.9042494

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…This modeling approach gives several straightforward measures of network performance that can be examined and utilized using the curves we have just defined, such as backlog and virtual delay. Since backlog is a measure of how many bits are inside a system at time t, it can be defined as the difference between the number of bits input to the system and the number of bits output, as shown in (11).…”

Section: Preliminariesmentioning

confidence: 99%

“…Considering both the IEEE 13-bus feeder and the communications network shown in Figure 2, we can utilize the approach developed in [11] to develop a directed graph of the entire CPS with both the communications network and the power system. This is shown in Figure 5, and shows how the physical system interacts with the communications network.…”

Section: Ieee 13-bus Use Casementioning

confidence: 99%

“…Here we see that the impact to gateway 645 is affecting the communication times for the two DERs that must communicate through that gateway, but not the other routes. Likewise, the backlog of traffic at each node can be calculated using (11), and the bounds for the overall backlog along a network data flow can be calculated using ( 6) and ( 13). If we apply (11) for our scenario to calculate the backlog at each node in the system, we will see the results shown in Table 4.…”

Section: Scenariosmentioning

confidence: 99%

“…In [11], Jacobs et al developed an approach to define cyber-physical observability by combining physical observability algorithms with graph-theoretic network observability methods. Specifically, a combined cyber-physical directed graph was developed with both physical grid and communication infrastructure components, and both network and physical (e.g., phasor measurement unit (PMU) placement algorithms) observability methods were applied to arrive at the cyber-physical observability definition.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Data Flows with Network Calculus in Cyber-Physical Systems: Enabling Feature Analysis for Anomaly Detection Applications

2021

Self Cite

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The electric grid is becoming increasingly cyber-physical with the addition of smart technologies, new communication interfaces, and automated grid-support functions. Because of this, it is no longer sufficient to only study the physical system dynamics, but the cyber system must also be monitored as well to examine cyber-physical interactions and effects on the overall system. To address this gap for both operational and security needs, cyber-physical situational awareness is needed to monitor the system to detect any faults or malicious activity. Techniques and models to understand the physical system (the power system operation) exist, but methods to study the cyber system are needed, which can assist in understanding how the network traffic and changes to network conditions affect applications such as data analysis, intrusion detection systems (IDS), and anomaly detection. In this paper, we examine and develop models of data flows in communication networks of cyber-physical systems (CPSs) and explore how network calculus can be utilized to develop those models for CPSs, with a focus on anomaly and intrusion detection. This provides a foundation for methods to examine how changes to behavior in the CPS can be modeled and for investigating cyber effects in CPSs in anomaly detection applications.

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Section: Preliminariesmentioning

confidence: 99%

Section: Ieee 13-bus Use Casementioning

confidence: 99%

Section: Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling Data Flows with Network Calculus in Cyber-Physical Systems: Enabling Feature Analysis for Anomaly Detection Applications

2021

Self Cite

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“…Similarly, an observability enhancing framework such as [10] discusses the feasibility only over cloud subsystem microservices. Systems such as [11], [12] for observability in IoT and edge networks usually do not integrate with cloud native applications and hence are not scalable onto the cloud subsystems [13]. The BonFIRE framework by Kavoussanakis et al [14] aims to create a testbed for cloud applications and allows the user to host wide scenarios of services that are not yet deployed to the cloud.…”

Section: Security-elasticity Dependenciesmentioning

confidence: 99%

On Analysis of Security and Elasticity Dependency in IIoT Platform Services

Raj

Truong

2021

2021 IEEE International Conference on Services Computing (SCC)

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Modern IIoT (Industrial Internet of Things) systems rely on highly decomposed platform services that are distributed across the edge and cloud. Security attacks in these systems can cause indirect and unwanted impacts on the elasticity of platform services. These impacts can often manifest across subsystems and not necessarily remain localized. However, there is a lack of techniques to identify and model cross-layered, cross-system concrete dependencies between specific security attacks and their corresponding elasticity impacts. This paper presents a novel framework SEDAICO that can model the elasticity relationship among IIoT platform services due to a security attack. Secondly, the framework supports the developer to identify concrete dependency between specific security attacks and the corresponding impacts on the elasticity of the platform services. We further illustrate our work with experiments and evaluate the framework through a real-world example.

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Leveraging graph clustering techniques for cyber‐physical system analysis to enhance disturbance characterisation

Jacobs,

Hossain‐McKenzie,

Sun

et al. 2024

IET Cyber-Phy Sys Theory & Ap

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Cyber‐physical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyber‐physical systems requires improved understanding of the combined cyber‐physical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyber‐physical data from smart grid systems to analyse differences and similarities in behaviour during cyber‐, physical‐, and cyber‐physical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyber‐physical systems. Through this analysis, deeper insights can be shared with decision‐makers on what cyber and physical components are strongly or weakly linked, what cyber‐physical pathways are most traversed, and the criticality of certain cyber‐physical nodes or edges. This paper presents several types of clustering methods for cyber‐physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber‐physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber‐physical graph interdependency analysis.

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Cyber-Physical Observability for the Electric Grid

Cited by 11 publications

References 12 publications

Modeling Data Flows with Network Calculus in Cyber-Physical Systems: Enabling Feature Analysis for Anomaly Detection Applications

Modeling Data Flows with Network Calculus in Cyber-Physical Systems: Enabling Feature Analysis for Anomaly Detection Applications

On Analysis of Security and Elasticity Dependency in IIoT Platform Services

Leveraging graph clustering techniques for cyber‐physical system analysis to enhance disturbance characterisation

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