Efficiently and Effectively: A Two-stage Approach to Balance Plaintext and Encrypted Text for Traffic Classification

TL;DR

This paper introduces a two-stage traffic classification method that efficiently balances the use of plaintext and encrypted data, improving accuracy and speed in identifying network applications.

Contribution

It presents a novel two-phase approach with a selector to determine when plaintext suffices, enhancing both effectiveness and efficiency in encrypted traffic classification.

Findings

Achieves state-of-the-art accuracy on two datasets.

Reduces classification time by selectively using plaintext.

Improves overall model efficiency without sacrificing accuracy.

Abstract

Encrypted traffic classification is the task of identifying the application or service associated with encrypted network traffic. One effective approach for this task is to use deep learning methods to encode the raw traffic bytes directly and automatically extract features for classification (byte-based models). However, current byte-based models input raw traffic bytes, whether plaintext or encrypted text, for automated feature extraction, neglecting the distinct impacts of plaintext and encrypted text on downstream tasks. Additionally, these models primarily focus on improving classification accuracy, with little emphasis on the efficiency of models. In this paper, for the first time, we analyze the impact of plaintext and encrypted text on the model's effectiveness and efficiency. Based on our observations and findings, we propose a two-phase approach to balance the trade-off between plaintext and encrypted text in traffic classification. Specifically, Stage one is to Determine whether the Plain text is enough to be accurately Classified (DPC) using the proposed DPC Selector. This stage quickly identifies samples that can be classified using plaintext, leveraging explicit byte features in plaintext to enhance model's efficiency. Stage two aims to adaptively make a classification with the result from stage one. This stage incorporates encrypted text information for samples that cannot be classified using plaintext alone, ensuring the model's effectiveness on traffic classification tasks. Experiments on two datasets demonstrate that our proposed model achieves state-of-the-art results in both effectiveness and efficiency.