Power systems data fusion based on belief propagation

Fusco, Francesco; Tirupathi, Seshu; Gormally, Robert

doi:10.1109/isgteurope.2017.8260301

Cited by 10 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It provides a wide range of data at one place, is easily accessible, clean and ready-to-use, permanently available and versioncontrolled. The large number of users -around 100, 000 unique visitors during 2017) -and, more importantly, the amount of research that makes use of OPSD -26 published papers by the time of writing since the go-live in late 2016, out of which 12 are published papers in high quality journals indexed in the SCI/SSCI [13,[40][41][42][43][44][45][46][47][48][49][50] and 14 in other journals, conference papers, books and grey literature [51][52][53][54][55][56][57][58][59][60][61][62][63][64] -suggest that the platform fulfils a need and provides value to electricity system modellers.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Open Power System Data – Frictionless data for electricity system modelling

et al. 2019

View full text Add to dashboard Cite

The quality of electricity system modelling heavily depends on the input data used. Although a lot of data is publicly available, it is often dispersed, tedious to process and partly contains errors. We argue that a central provision of input data for modelling has the character of a public good: it reduces overall societal costs for quantitative energy research as redundant work is avoided, and it improves transparency and reproducibility in electricity system modelling. This paper describes the Open Power System Data platform that aims at realising the efficiency and quality gains of centralised data provision by collecting, checking, processing, aggregating, documenting and publishing data required by most modellers. We conclude that the platform can provide substantial benefits to energy system analysis by raising efficiency of data pre-processing, providing a method for making data pre-processing for energy system modelling traceable, flexible and reproducible and improving the quality of original data published by data providers.

show abstract

Section: Discussion and Outlookmentioning

confidence: 99%

Open Power System Data – Frictionless data for electricity system modelling

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Existing works do not consider the fusion of cyber and physical attributes for intrusion detection together. A probabilistic graphic model (PGM) based power systems data fusion is proposed in [37], where the state variables are estimated based on the measurements from heterogeneous sources by belief propagation using factor graphs. These PGM models require the knowledge of the priors of the state variables, and also assume the measurements to be trustworthy.…”

Section: Data Fusion In Power Systemsmentioning

confidence: 99%

Multi-Source Multi-Domain Data Fusion for Cyberattack Detection in Power Systems

Sahu

Mao

Wlazlo³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Modern power systems equipped with advanced communication infrastructure are cyberphysical in nature. The traditional approach of leveraging physical measurements for detecting cyberinduced physical contingencies are insufficient to reflect the accurate cyber-physical states. Moreover, deploying conventional rule-based and anomaly-based intrusion detection systems for cyberattack detection results in higher false positives. Hence, independent usage of detection tools of cyberattacks in cyber and physical sides has a limited capability. In this work, a mechanism to fuse real-time data from cyber and physical domains, to improve situational awareness of the whole system is developed. It is demonstrated how improved situational awareness can help reduce false positives in intrusion detection. This cyber and physical data fusion results in cyber-physical state space explosion which is addressed using different feature transformation and selection techniques. Our fusion engine is further integrated into a cyber-physical power system testbed as an application that collects cyber and power system telemetry from multiple sensors emulating real-world data sources found in a utility. These are synthesized into features for algorithms to detect cyber intrusions. Results are presented using the proposed data fusion application to infer False Data and Command Injection (FDI and FCI)-based Man-in-The-Middle attacks. Post collection, the data fusion application uses time-synchronized merge and extracts features. This is followed by pre-processing such as imputation, categorical encoding, and feature reduction, before training supervised, semi-supervised, and unsupervised learning models to evaluate the performance of the intrusion detection system. A major finding is the improvement of detection accuracy by fusion of features from cyber, security, and physical domains. Additionally, it is observed that the semi-supervised co-training technique to perform at par with supervised learning methods with the proposed feature vector. The approach and toolset as well as the dataset that are generated can be utilized to prevent threats such as false data or command injection attacks from being carried out by identifying cyber intrusions accurately.

show abstract

“…Existing works do not consider fusion of cyber and physical attributes for intrusion detection together. A probabilistic graphic model based power systems data fusion is proposed in [22], where the state variables are estimated based on the measurements from heterogeneous sources by belief propagation using factor graphs. These probabilistic models require the knowledge of the priors of the state variables and also assume the measurements to be trustworthy.…”

Section: B Multi-sensor Fusion Applicationsmentioning

confidence: 99%

Multi-Source Data Fusion for Cyberattack Detection in Power Systems

Sahu,

Mao,

Wlazlo

et al. 2021

Preprint

View full text Add to dashboard Cite

can cause a severe impact on power systems unless detected early. However, accurate and timely detection in critical infrastructure systems presents challenges, e.g., due to zero-day vulnerability exploitations and the cyberphysical nature of the system coupled with the need for high reliability and resilience of the physical system. Conventional rule-based and anomaly-based intrusion detection system (IDS) tools are insufficient for detecting zero-day cyber intrusions in the industrial control system (ICS) networks. Hence, in this work, we show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives. Specifically, we present how to recognize and address the barriers that can prevent the accurate use of multiple data sources for fusion-based detection. We perform multi-source data fusion for training IDS in a cyber-physical power system testbed where we collect cyber and physical side data from multiple sensors emulating real-world data sources that would be found in a utility and synthesizes these into features for algorithms to detect intrusions. Results are presented using the proposed data fusion application to infer False Data and Command injectionbased Man-in-The-Middle (MiTM) attacks. Post collection, the data fusion application uses time-synchronized merge and extracts features followed by pre-processing such as imputation and encoding before training supervised, semi-supervised, and unsupervised learning models to evaluate the performance of the IDS. A major finding is the improvement of detection accuracy by fusion of features from cyber, security, and physical domains. Additionally, we observed the co-training technique performs at par with supervised learning methods when fed with our features.

show abstract

Power systems data fusion based on belief propagation

Cited by 10 publications

References 19 publications

Open Power System Data – Frictionless data for electricity system modelling

Open Power System Data – Frictionless data for electricity system modelling

Multi-Source Multi-Domain Data Fusion for Cyberattack Detection in Power Systems

Multi-Source Data Fusion for Cyberattack Detection in Power Systems

Contact Info

Product

Resources

About