# UDFS: Lightweight Representation-Driven Open World Robust Encrypted Traffic Classification

arXiv: https://arxiv.org/abs/2509.11157 · HTML: https://mlsanity.com/paper/2509.11157/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [More Than Meets the Eye: A Semantics-Aware Traffic Augmentation Framework for Generalizable Website Fingerprinting](https://mlsanity.com/paper/2605.11402/) (2026)

## Abstract (arXiv)

In recent years, sequence features such as packet length have received considerable attention due to their central role in encrypted traffic analysis. Existing sequence modeling approaches can be broadly categorized into flow-level and trace-level methods: the former suffer from high feature redundancy, limiting their discriminative power, whereas the latter preserve complete information but incur substantial computational and storage overhead. To address these limitations, we propose the Up-Down Flow Sequence (UDFS) representation, which compresses an entire trace into a two-dimensional sequence and characterizes each flow by the aggregate of its upstream and downstream traffic, reducing complexity while maintaining high discriminability. Furthermore, to address the challenge of class-specific discriminability differences, we propose an adaptive threshold mechanism that dynamically adjusts training weights and rejection boundaries, enhancing the model's classification performance. Experimental results demonstrate that the proposed method achieves superior classification performance and robustness on both coarse-grained and fine-grained datasets, as well as under concept drift and open-world scenarios. Code and Dataset are available at https://github.com/kid1999/UDFS.