Degree-based Outliers Detection within IP Traffic Modelled as a Link Stream

Audrey Wilmet (1) , Tiphaine Viard (1) , Matthieu Latapy (1) , Robin Lamarche-Perrin (2)

1 ComplexNetworks
2 ISC-PIF - Institut des Systèmes Complexes - Paris Ile-de-France

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.

Domaines: Informatique [cs]; Réseaux et télécommunications [cs.NI]; Réseaux sociaux et d'information [cs.SI]

Audrey Wilmet, Tiphaine Viard, Matthieu Latapy, Robin Lamarche-Perrin. Degree-based Outliers Detection within IP Traffic Modelled as a Link Stream. 2018 Network Traffic Measurement and Analysis Conference (TMA), Jun 2018, Vienna, Austria. pp.1-8, ⟨10.23919/TMA.2018.8506575⟩. ⟨hal-02085261⟩