Print Email Facebook Twitter Anomaly detection in diurnal data Title Anomaly detection in diurnal data Author Mata, F. Zuraniewski, P.W. Mandjes, M. Mellia, M. Publication year 2014 Abstract In this paper we present methodological advances in anomaly detection tailored to discover abnormal traffic patterns under the presence of seasonal trends in data. In our setup we impose specific assumptions on the traffic type and nature; our study features VoIP call counts, for which several traces of real data has been used in this study, but the methodology can be applied to any data following, at least roughly, a non-homogeneous Poisson process (think of highly aggregated traffic flows). A performance study of the proposed methods, covering situations in which the assumptions are fulfilled as well as violated, shows good results in great generality. Finally, a real data example is included showing how the system could be implemented in practice. © 2013 Elsevier B.V. All rights reserved. Subject Communication & InformationPNS - Performance of Networks & ServicesTS - Technical SciencesInfrastructuresInformaticsInformation SocietyAnomaly detectionChangepointDetrendingDiurnal patternVoIP To reference this document use: http://resolver.tudelft.nl/uuid:217f2937-86e5-481c-8d87-fa03a6ff47a8 DOI https://doi.org/10.1016/j.bjp.2013.11.011 TNO identifier 492969 Source Computer Networks, 60, 187-200 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.