Authors:
Gerard Draper-Gil
;
Arash Habibi Lashkari
;
Mohammad Saiful Islam Mamun
and
Ali A. Ghorbani
Affiliation:
University of New Brunswick, Canada
Keyword(s):
Traffic Classification, Encrypted Traffic Characterization, Flow Time-based Features, VPN Traffic Characterization, Flow Timeout Value.
Related
Ontology
Subjects/Areas/Topics:
Access Control
;
Data Engineering
;
Databases and Data Security
;
Information and Systems Security
;
Information Assurance
;
Information Hiding
;
Internet Technology
;
Web Information Systems and Technologies
Abstract:
Traffic characterization is one of the major challenges in today’s security industry. The continuous evolution
and generation of new applications and services, together with the expansion of encrypted communications
makes it a difficult task. Virtual Private Networks (VPNs) are an example of encrypted communication service
that is becoming popular, as method for bypassing censorship as well as accessing services that are geographically
locked. In this paper, we study the effectiveness of flow-based time-related features to detect VPN traffic
and to characterize encrypted traffic into different categories, according to the type of traffic e.g., browsing,
streaming, etc. We use two different well-known machine learning techniques (C4.5 and KNN) to test the accuracy
of our features. Our results show high accuracy and performance, confirming that time-related features
are good classifiers for encrypted traffic characterization.