Data-driven Stochastic Anomaly Detection on Smart-Grid communications using Mixture Poisson Distributions

Marino, Daniel L.; Wickramasinghe, Chathurika S.; Rieger, Craig; Manic, Milos

doi:10.1109/iecon.2019.8927060

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…The increased dependency on information and communication technologies (ICTs) has made power systems increasingly vulnerable to various cyber-physical attacks [108]. These attacks range from reconnaissance attacks, the objective of which is to gain information on the system, to attacks that attempt to disrupt the system such as denial of service (DoS), replay, or data-insertion attacks [109,110]. DoS attacks are some of the most-common approaches to disrupt communication networks.…”

Section: Cyber-attack Vectorsmentioning

confidence: 99%

Review of Design Elements within Power Infrastructure Cyber–Physical Test Beds as Threat Analysis Environments

et al. 2021

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Cyber–physical systems (CPSs) are an integral part of modern society; thus, enhancing these systems’ reliability and resilience is paramount. Cyber–physical testbeds (CPTs) are a safe way to test and explore the interplay between the cyber and physical domains and to cost-effectively enhance the reliability and resilience of CPSs. Here a review of CPT elements, broken down into physical components (simulators, emulators, and physical hardware), soft components (communication protocols, network timing protocols), and user interfaces (visualization-dashboard design considerations) is presented. Various methods used to validate CPS performance are reviewed and evaluated for potential applications in CPT performance validation. Last, initial simulated results for a CPT design, based on the IEEE 33 bus system, are presented, along with a brief discussion on how model-based testing and fault–injection-based testing (using scaling and ramp-type attacks) may be used to help validate CPT performance.

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Section: Cyber-attack Vectorsmentioning

confidence: 99%

Review of Design Elements within Power Infrastructure Cyber–Physical Test Beds as Threat Analysis Environments

et al. 2021

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“…Existing approaches demonstrate the capability of ML models to identify abnormal cyber behavior [3], [5]- [7]. However, these approaches often lack the interpretability of the results as they only report an anomaly without any further information about the source or cause of the anomaly.…”

Section: Introductionmentioning

confidence: 99%