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Infrastructure anomaly detection: A cloud-native architecture at Germany’s Federal Employment Agency

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dc.contributor.authorHerget,Gebhard
dc.contributor.authorSultanow,Eldar
dc.contributor.authorChircu,Alina
dc.contributor.authorLudsteck,Johannes
dc.contributor.authorHammer,Sebastian
dc.contributor.authorKoch,Christian
dc.contributor.authorReuter,Willy
dc.contributor.authorSeßler,Matthias
dc.contributor.editorDemmler, Daniel
dc.contributor.editorKrupka, Daniel
dc.contributor.editorFederrath, Hannes
dc.date.accessioned2022-09-28T17:10:50Z
dc.date.available2022-09-28T17:10:50Z
dc.date.issued2022
dc.description.abstractIn prior research we explored the use of time series analysis methods to detect one class of information technology (IT) infrastructure anomalies - Distributed Denial of Service (DDoS) attacks. The results of this prior work were a mathematical model and a prototype implementation that were concretely trialed and operated in the data centers of Germany's Federal Employment Agency (FEA). With this paper, we go one step further and generalize as well as optimize the mathematical model and create higher performance and scalability for an updated prototype through targeted use of cloud technologies. The starting point of our generalization is the Exponential Smoothing (E-S) approach, which underlies, for example, the well-known Holt-Winters method. This method is used to predict univariate time series. To detect anomalies (such as DDoS attacks) in infrastructure data, we extend the E-S approach to enable it to forecast multivariate time series. In this optimization of our method and our prototype, we take an exploratory, agile approach. Furthermore, we present a cloud-native architecture stack which we pilot in Azure.en
dc.identifier.doi10.18420/inf2022_101
dc.identifier.isbn978-3-88579-720-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39581
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-326
dc.subjectTime Series
dc.subjectEnterprise Architecture
dc.subjectExponential Smoothing
dc.subjectCloud
dc.subjectAzure
dc.subjectDistributed Denial of Service
dc.subjectInfrastructure Anomaly Detection System
dc.titleInfrastructure anomaly detection: A cloud-native architecture at Germany’s Federal Employment Agencyen
gi.citation.endPage1193
gi.citation.startPage1181
gi.conference.date26.-30. September 2022
gi.conference.locationHamburg
gi.conference.sessiontitle(Agiles) Enterprise Architecture Management in Forschung und Praxis

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P326 - INFORMATIK 2022 - Informatik in den Naturwissenschaften