  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.
