AwareNet: Using WSBMs for Network Traffic Analyis

Maximilian Stephan, Patrick Krämer, Wolfgang Kellerer

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

Abstract

Characterizing network behavior, an essential building block for many network management tasks becomes increasingly difficult for administrators. Reasons for that are, among others, rising traffic volume and dynamicity in modern networks. To automate network behavior characterization, we present AwareNet, a system that uses Weighted Stochastic Block Models (WSBMs). By providing insight into network-inherent dynamics AwareNet supports administrators in handling the rising traffic volume and dynamicity. As an example, we show that AwareNet can detect targeted host scans in a campus network.

Original languageEnglish
Title of host publicationCoNEXT-SW 2022 - Proceedings of the International CoNEXT Student Workshop 2022, Part CoNEXT 2022
PublisherAssociation for Computing Machinery, Inc
Pages35-36
Number of pages2
ISBN (Electronic)9781450399371
DOIs
StatePublished - 9 Dec 2022
Event3rd ACM International CoNEXT Student Workshop, CoNEXT-SW 2022, co-located with the 18th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2022 - Rome, Italy
Duration: 9 Dec 2022 → …

Publication series

NameCoNEXT-SW 2022 - Proceedings of the International CoNEXT Student Workshop 2022, Part CoNEXT 2022

Conference

Conference3rd ACM International CoNEXT Student Workshop, CoNEXT-SW 2022, co-located with the 18th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2022
Country/TerritoryItaly
CityRome
Period9/12/22 → …

Keywords

  • anomaly detection
  • network traffic analysis
  • stochastic block model

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