Trust and reputation approach to smart grid security

Pradhan, Omkar; Awan, Muhammad Faheem; Newman, Keith; Barnes, Frank S.

doi:10.1109/isrcs.2011.6016097

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…In tackling meter tampering within the AMI, Pradhan et al did the reverse of calculating trust by using mistrust [40]. Their algorithm involved comparing the presented data with houses and actual data from smart meters to see whether a house is being truthful or not.…”

Section: Research Areasmentioning

confidence: 99%

A Trust-Influenced Smart Grid: A Survey and a Proposal

Boakye-Boateng

Ghorbani

Lashkari

2022

JSAN

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A compromised Smart Grid, or its components, can have cascading effects that can affect lives. This has led to numerous cybersecurity-centric studies focusing on the Smart Grid in research areas such as encryption, intrusion detection and prevention, privacy and trust. Even though trust is an essential component of cybersecurity research; it has not received considerable attention compared to the other areas within the context of Smart Grid. As of the time of this study, we observed that there has neither been a study assessing trust within the Smart Grid nor were there trust models that could detect malicious attacks within the substation. With these two gaps as our objectives, we began by presenting a mathematical formalization of trust within the context of Smart Grid devices. We then categorized the existing trust-based literature within the Smart Grid under the NIST conceptual domains and priority areas, multi-agent systems and the derived trust formalization. We then proposed a novel substation-based trust model and implemented a Modbus variation to detect final-phase attacks. The variation was tested against two publicly available Modbus datasets (EPM and ATENA H2020) under three kinds of tests, namely external, internal, and internal with IP-MAC blocking. The first test assumes that external substation adversaries remain so and the second test assumes all adversaries within the substation. The third test assumes the second test but blacklists any device that sends malicious requests. The tests were performed from a Modbus server’s point of view and a Modbus client’s point of view. Aside from detecting the attacks within the dataset, our model also revealed the behaviour of the attack datasets and their influence on the trust model components. Being able to detect all labelled attacks in one of the datasets also increased our confidence in the model in the detection of attacks in the other dataset. We also believe that variations of the model can be created for other OT-based protocols as well as extended to other critical infrastructures.

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Section: Research Areasmentioning

confidence: 99%

A Trust-Influenced Smart Grid: A Survey and a Proposal

Boakye-Boateng

Ghorbani

Lashkari

2022

JSAN

View full text Add to dashboard Cite

show abstract

“…For these reasons, various studies exist that have explored how to integrate reputation-based trust management systems in smart grids. For instance, in [342] a trust management methodology is developed to verify if individual smart meters generate selfish and malicious reports on energy usage. Furthermore, one of the advantages of a trust system is to provide an additional layer of protection in power systems implementing agent-based control functions as discussed in [343,344].…”

Section: Security Analysismentioning

confidence: 99%