  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.
