  Network traffic classification that is widely applicable and highly accurate
is valuable for many network security and management tasks. A flexible and
easily configurable classification framework is ideal, as it can be customized
for use in a wide variety of networks. In this paper, we propose a highly
configurable and flexible machine learning traffic classification method that
relies only on statistics of sequences of packets to distinguish known, or
approved, traffic from unknown traffic. Our method is based on likelihood
estimation, provides a measure of certainty for classification decisions, and
can classify traffic at adjustable certainty levels. Our classification method
can also be applied in different classification scenarios, each prioritizing a
different classification goal. We demonstrate how our classification scheme and
all its configurations perform well on real-world traffic from a high
performance computing network environment.
