Abstract : This paper presents current work for the detection of anomalies in Netflow records by leveraging a kernel function method. Netflow records are spatially aggregated over time, such that the designed kernel function can capture topological and quantitative changes in network traffic time series.
Ramin Sadre; Jiří Novotný; Pavel Čeleda; Martin Waldburger; Burkhard Stiller. 6th International Conference on Autonomous Infrastructure (AIMS), Jun 2012, Luxembourg, Luxembourg. Springer, Lecture Notes in Computer Science, LNCS-7279, pp.122-125, 2012, Dependable Networks and Services. 〈10.1007/978-3-642-30633-4_16〉
https://hal.inria.fr/hal-01529787
Contributeur : Hal Ifip
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Soumis le : mercredi 31 mai 2017 - 13:17:49
Dernière modification le : mercredi 31 mai 2017 - 13:19:35
Document(s) archivé(s) le : mercredi 6 septembre 2017 - 16:06:02
Cynthia Wagner, Thomas Engel. Detecting Anomalies in Netflow Record Time Series by Using a Kernel Function. Ramin Sadre; Jiří Novotný; Pavel Čeleda; Martin Waldburger; Burkhard Stiller. 6th International Conference on Autonomous Infrastructure (AIMS), Jun 2012, Luxembourg, Luxembourg. Springer, Lecture Notes in Computer Science, LNCS-7279, pp.122-125, 2012, Dependable Networks and Services. 〈10.1007/978-3-642-30633-4_16〉. 〈hal-01529787〉
