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

Audrey Wilmet (1) , Tiphaine Viard (2) , Matthieu Latapy (3, 1) , Robin Lamarche-Perrin (4)

1 ComplexNetworks
2 RIKEN AIP - RIKEN Center for Advanced Intelligence Project [Tokyo]
3 LIP - Laboratoire de l'Informatique du Parallélisme
4 ISC-PIF - Institut des Systèmes Complexes - Paris Ile-de-France

This paper aims at precisely detecting and identifying anomalous events in IP traffic. To this end, we adopt the link stream formalism which properly captures temporal and structural features of the data. Within this framework, we focus on finding anomalous behaviours with respect to the degree of IP addresses over time. Due to diversity in IP profiles, this feature is typically distributed heterogeneously, preventing us to directly 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 several MAWI captures of IP traffic and we show that it succeeds in detecting relevant patterns in terms of anomalous network activity.

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

Audrey Wilmet, Tiphaine Viard, Matthieu Latapy, Robin Lamarche-Perrin. Degree-based Outlier Detection within IP Traffic Modelled as a Link Stream. Computer Networks, 2019, 161, pp.197-209. ⟨10.1016/j.comnet.2019.07.002⟩. ⟨hal-02172934⟩