Degree-Based Outliers Detection Within IP Traffic Modelled as a Link Stream

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Precise detection and identification of anomalous events in IP traffic are crucial in many applications. This paper intends to address this task by adopting the link stream formalism which properly captures temporal and structural features of the data. Within this framework we focus on finding anomalous behaviours with the degree of IP addresses over time. Due to diversity in IP profiles, this feature is typically distributed heterogeneously, preventing us to find anomalies. To deal with this challenge, we design a method to detect outliers as well as precisely identify their cause in a sequence of similar heterogeneous distributions. We apply it to a MAWI capture of IP traffic and we show that it succeeds at detecting relevant patterns in terms of anomalous network activity.

Original languageEnglish
Title of host publicationTMA 2018 - Proceedings of the 2nd Network Traffic Measurement and Analysis Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9783903176096
DOIs
Publication statusPublished - 23 Oct 2018
Event2nd Network Traffic Measurement and Analysis Conference, TMA 2018 - Vienna, Austria
Duration: 26 Jun 201829 Jun 2018

Publication series

NameTMA 2018 - Proceedings of the 2nd Network Traffic Measurement and Analysis Conference

Conference

Conference2nd Network Traffic Measurement and Analysis Conference, TMA 2018
Country/TerritoryAustria
CityVienna
Period26/06/1829/06/18

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