  Accurate and efficient network traffic classification is important for many
network management tasks, from traffic prioritization to anomaly detection.
Although classifiers using pre-computed flow statistics (e.g., packet sizes,
inter-arrival times) can be efficient, they may experience lower accuracy than
techniques based on raw traffic, including packet captures. Past work on
representation learning-based classifiers applied to network traffic captures
has shown to be more accurate, but slower and requiring considerable additional
memory resources, due to the substantial costs in feature preprocessing. In
this paper, we explore this trade-off and develop the Adaptive
Constraint-Driven Classification (AC-DC) framework to efficiently curate a pool
of classifiers with different target requirements, aiming to provide comparable
classification performance to complex packet-capture classifiers while adapting
to varying network traffic load.
  AC-DC uses an adaptive scheduler that tracks current system memory
availability and incoming traffic rates to determine the optimal classifier and
batch size to maximize classification performance given memory and processing
constraints. Our evaluation shows that AC-DC improves classification
performance by more than 100% compared to classifiers that rely on flow
statistics alone; compared to the state-of-the-art packet-capture classifiers,
AC-DC achieves comparable performance (less than 12.3% lower in F1-Score), but
processes traffic over 150x faster.
