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dc.contributor.authorJadidi, Z
dc.contributor.authorDorri, A
dc.contributor.authorJurdak, R
dc.contributor.authorFidge, C
dc.contributor.editorWang, GJ
dc.contributor.editorKo, R
dc.contributor.editorBhuiyan, MZA
dc.contributor.editorPan, Y
dc.date.accessioned2022-04-22T01:47:23Z
dc.date.available2022-04-22T01:47:23Z
dc.date.issued2020
dc.identifier.isbn9781665403924
dc.identifier.issn2324-898X
dc.identifier.doi10.1109/TrustCom50675.2020.00262
dc.identifier.urihttp://hdl.handle.net/10072/414115
dc.description.abstractDue to the rise of Industrial Control Systems (ICSs) cyber-attacks in the recent decade, various security frameworks have been designed for anomaly detection. While advanced ICS attacks use sequential phases to launch their final attacks, existing anomaly detection methods can only monitor a single source of data. However, analysis of multiple security data could provide more comprehensive and system-wide anomaly detection in industrial networks. In this paper, we present an anomaly detection framework for ICSs that consists of two stages: i) blockchain-based log management where the logs of ICS devices are collected in a secure and distributed manner, and ii) multi-source anomaly detection where the blockchain logs are analysed using multi-source deep learning which in turn provides a system wide anomaly detection method. We validated our framework using two ICS datasets: a factory automation dataset and a Secure Water Treatment (SWaT) dataset. These datasets contain physical and network level normal and abnormal traffic. The performance of our new framework is compared with single-source machine learning methods. The precision of our framework is 95% which is comparable with single-source anomaly detectors. However, multi-source analysis is more robust because it can detect anomalies from multiple sources simultaneously, while achieving comparable precision for each of the sources.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.relation.ispartofconferencename19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)
dc.relation.ispartofconferencetitle2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)
dc.relation.ispartofdatefrom2020-12-29
dc.relation.ispartofdateto2021-01-01
dc.relation.ispartoflocationGuangzhou, China
dc.relation.ispartofpagefrom1920
dc.relation.ispartofpageto1925
dc.subject.fieldofresearchBusiness information systems
dc.subject.fieldofresearchCybersecurity and privacy
dc.subject.fieldofresearchcode350303
dc.subject.fieldofresearchcode4604
dc.subject.keywordsScience & Technology
dc.subject.keywordsTechnology
dc.subject.keywordsComputer Science, Hardware & Architecture
dc.subject.keywordsComputer Science, Information Systems
dc.subject.keywordsComputer Science, Theory & Methods
dc.titleSecuring Manufacturing Using Blockchain
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationJadidi, Z; Dorri, A; Jurdak, R; Fidge, C, Securing Manufacturing Using Blockchain, 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2020, pp. 1920-1925
dc.date.updated2022-04-22T01:44:46Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorJadidi, Zahra


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