Show simple item record

dc.contributor.authorAmin, BMR
dc.contributor.authorTaghizadeh, S
dc.contributor.authorMaric, S
dc.contributor.authorHossain, MJ
dc.contributor.authorAbbas, R
dc.date.accessioned2021-10-22T03:34:50Z
dc.date.available2021-10-22T03:34:50Z
dc.date.issued2021
dc.identifier.issn1932-8184
dc.identifier.doi10.1109/JSYST.2020.3001951
dc.identifier.urihttp://hdl.handle.net/10072/409391
dc.description.abstractFalse data injection attack (FDIA) is a critical cyber-attack that can cause disrupt operations and subsequently blackouts in smart grid networks. Cleverly constructed stealthy false measurement vectors can circumvent the bad data detector unit and mislead the state estimation process. This article proposes a novel belief propagation (BP)-based algorithm to detect both random and stealthy-type FDIAs in smart grids with higher detection rate than the state-of-the-art machine learning classifiers such as Naive Bayes, support vector machines, Random Forest, OneR, and AdaBoost. Another novel feature of the proposed algorithm is to detect FDIAs without using any historical cyber-attack data, which are sketchy due to security constraints and infinitesimal in occurrence numbers. The proposed BP method utilizes local sensor measurement data to calculate local belief and send it as a message signal to the control center. Then, the control center determines final/global belief and compares the result with a predefined threshold value derived from the uncompromised measurement database. The real-time steady-state load data are utilized for dc state estimation. From the obtained results, performance parameters such as detection rate, receiver operating characteristic curve, precision, recall, and F-measure of the proposed BP algorithm are found superior to the aforementioned state-of-the-art machine learning algorithms.
dc.description.peerreviewedYes
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofpagefrom2046
dc.relation.ispartofpageto2057
dc.relation.ispartofissue2
dc.relation.ispartofjournalIEEE Systems Journal
dc.relation.ispartofvolume15
dc.subject.fieldofresearchElectrical engineering
dc.subject.fieldofresearchElectronics, sensors and digital hardware
dc.subject.fieldofresearchcode4008
dc.subject.fieldofresearchcode4009
dc.titleSmart Grid Security Enhancement by Using Belief Propagation
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationAmin, BMR; Taghizadeh, S; Maric, S; Hossain, MJ; Abbas, R, Smart Grid Security Enhancement by Using Belief Propagation, IEEE Systems Journal, 2021, 15 (2), pp. 2046-2057
dc.date.updated2021-10-22T03:32:44Z
gro.hasfulltextNo Full Text
gro.griffith.authorTaghizadeh, Foad
gro.griffith.authorHossain, Jahangir


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record