Machine learning applications for real-time detection of cyber-physical attacks on water distribution systems

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dc.contributor.author Τσιάμη, Λυδία-Μαρία el
dc.contributor.author Tsiami, Lydia-Maria en
dc.date.accessioned 2020-12-14T16:54:01Z
dc.date.available 2020-12-14T16:54:01Z
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/52513
dc.identifier.uri http://dx.doi.org/10.26240/heal.ntua.20211
dc.rights Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/gr/ *
dc.subject Δίκτυα διανομής νερού el
dc.subject Μηχανική μάθηση el
dc.subject Στοχαστικά μοντέλα el
dc.subject Διάγνωση κυβερνοεπιθέσεων el
dc.subject Τεχνητά νευρωνικά δίκτυα el
dc.subject Water distribution networks en
dc.subject Machine learning en
dc.subject Stochastic models en
dc.subject Cyber-attack detection en
dc.subject Artificial Neural Networks en
dc.title Machine learning applications for real-time detection of cyber-physical attacks on water distribution systems en
dc.title Εφαρμογές Μηχανικής Μάθησης στη διάγνωση κυβερνοεπιθέσεων σε εταιρείες νερού σε πραγματικό χρόνο el
heal.type bachelorThesis
heal.classification Διαχείριση Υδατικών Πόρων el
heal.classification Μηχανική Μάθηση el
heal.language el
heal.language en
heal.access free
heal.recordProvider ntua el
heal.publicationDate 2020-11-04
heal.abstract Water distribution networks (WDN) deploy digital devices not only to monitor and control utility operations but also to increase automation and ultimately their efficiency. Although their digitalization is essential, it comes with a cost: it exposes the WDN to the risks of a Cyber-Physical System, i.e. cyber-attacks. The overall aim of this diploma thesis is to develop new and improve upon existing machine learning methods for cyber-physical attack detection on Water Distribution Networks. The innovation of this work resides in two main developments (a) the use of novel stochastic methods to generate the water demand timeseries needed to train existing machine learning models, in an effort to improve their overall performance in the presence of uncertainty and (b) the exploration and use of a novel family of machine learning methods that take both the spatial and temporal dimensions of a water network into account, in an effort to improve the ability of the model to represent the water network more accurately. To approach the first objective, we generate new, synthetic datasets for the study of cyber-physical attack detection on water distribution networks by performing simulations on a real medium size WDN under stochastically generated water demands. The second objective is approached by exploring the use of Spatio-Temporal Graph Neural Networks as cyber-physical attack detection tools. Finally, we test the detection performance of various ML algorithms (including SVDD, Autoencoder, Structural Convolutional Neural Networks) on our datasets and preexisting ones as well, and discuss. en
heal.advisorName Μακρόπουλος, Χρήστος el
heal.committeeMemberName Ευστρατιάδης, Ανδρέας el
heal.committeeMemberName Μαμάσης, Νίκος el
heal.committeeMemberName Μακρόπουλος, Χρήστος el
heal.academicPublisher Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Πολιτικών Μηχανικών. Τομέας Υδατικών Πόρων και Περιβάλλοντος el
heal.academicPublisherID ntua
heal.numberOfPages 161 σ. el
heal.fullTextAvailability false

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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα Except where otherwise noted, this item's license is described as Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα