Real-time anomaly detection at scale

Γαβαλάς, Νικόλαος; Gavalas, Nikolaos

dc.contributor.author	Γαβαλάς, Νικόλαος	el
dc.contributor.author	Gavalas, Nikolaos	en
dc.date.accessioned	2019-07-12T11:05:57Z
dc.date.available	2019-07-12T11:05:57Z
dc.date.issued	2019-07-12
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/49060
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.16500
dc.rights	Αναφορά Δημιουργού 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/gr/	*
dc.subject	Machine learning	en
dc.subject	Big data	en
dc.subject	Anomaly detection	en
dc.subject	Distributed stream processing	en
dc.subject	Real-time systems	en
dc.subject	Συστήματα πραγματικού χρόνου	el
dc.subject	Μηχανική μάθηση	el
dc.subject	Ανίχνευση ανωμαλιών	el
dc.subject	Κατανεμημένη επεργασία ροής	el
dc.title	Real-time anomaly detection at scale	en
heal.type	bachelorThesis
heal.classification	Computer engineering	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2019-03-13
heal.abstract	Anomaly Detection is a field of Machine Learning used by systems to identify observations that differ from the majority of data and are often linked directly to unexpected behaviour, errors, and other forms of novelties.Common applications of Anomaly Detection include but are not limited to credit card fraud detection, machinery or computer behaviour monitoring, network intrusion detection, real-time analytics, etc. In this thesis we study algorithms and methods for Anomaly Detection that enable identification of outliers both in real-time, in order to prevent unwanted events as soon as possible, and at a big scale, since the volume of data in our era is growing exponentially.	en
heal.advisorName	Κοζύρης, Νεκτάριος	el
heal.committeeMemberName	Κοζύρης, Νεκτάριος	el
heal.committeeMemberName	Γκούμας, Γεώργιος	el
heal.committeeMemberName	Τσουμάκος, Δημήτριος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών. Εργαστήριο Υπολογιστικών Συστημάτων	el
heal.academicPublisherID	ntua
heal.numberOfPages	71 σ.	el
heal.fullTextAvailability	true