2019/06/06 by Audrey Wilmet, Tiphaine Viard, Wilmet, Audrey +5
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Network Security and Intrusion Detection #Networking and Internet Architecture (cs.NI) #Social and Information Networks (cs.SI) #Software System Performance and Reliability #cs.NI #cs.SI
paper · pdf · doi:10.48550/arxiv.1906.02524
arxiv created 2019/06/06 · openalex publication_date 2019/06/06 · arxiv updated 2019/06/07 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
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.