# AC-DC: Adaptive Ensemble Classification for Network Traffic Identification

## TL;DR

AC-DC is an adaptive ensemble framework that balances accuracy and efficiency in network traffic classification by dynamically selecting classifiers based on system resources and traffic load, achieving high performance with reduced processing time.

## Contribution

The paper introduces AC-DC, a novel adaptive framework that efficiently combines classifiers to improve traffic classification accuracy and speed under resource constraints.

## Key findings

- AC-DC improves classification performance by over 100% compared to flow-statistics-based methods.
- It achieves comparable accuracy to packet-capture classifiers, with less than 12.3% lower F1-Score.
- Traffic processing speed is over 150 times faster than state-of-the-art packet-capture classifiers.

## Abstract

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.