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 |
Οι παρακάτω άδειες σχετίζονται με αυτό το τεκμήριο: