Behavioral clustering of non-stationary IP flow record data

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


Research units

  • University of Luxembourg
  • Delft University of Technology


Automated network traffic analysis using machine learning techniques plays an important role in managing networks and IT infrastructure. A key challenge to the correct and effective application of machine learning is dealing with non-stationary learning data sources and concept drift. Traffic evolves overtime due to new technology, software, services being used, changes in user behavior but also due to changes in network graphs like dynamic IP address assignment. In this paper, we present an automatic online method to detect change-points in network traffic based on IP flow record analysis. This technique is used to segment an observed behavior into smaller consecutive behaviors differing one from another. The segmented traffic is used to learn small communication profile characterizing accurately the activities present between two observed change-points. We validate our method using synthetic data and outline a real-world application to botnet hosts behavior modeling.


Original languageEnglish
Title of host publication2016 12th International Conference on Network and Service Management (CNSM)
Publication statusPublished - 19 Jan 2017
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Network and Service Management - Montreal, Canada
Duration: 31 Oct 20164 Nov 2016
Conference number: 12

Publication series

NameInternational Conference on Network and Service Management
ISSN (Print)2165-9605
ISSN (Electronic)2165-963X


ConferenceInternational Conference on Network and Service Management
Abbreviated titleCNSM

    Research areas

  • IP networks, Automata, Data models, Learning automata, Merging, Malware, Feature extraction

ID: 11559935