PacketPatch: Practical Generation and Deployment of Adversarial Packets for Byte-Feature-Based Encrypted Traffic Classification

Paper: PacketPatch: Practical Generation and Deployment of Adversarial Packets for Byte-Feature-Based Encrypted Traffic Classification Authors: Yuwei Xu, Yuanyuan Xu, Yunpeng Bai, Jiahui Chen, Kehui Song, Jie Cao, Qiao Xiang, Guang Cheng Affiliation: Southeast University, Purple Mountain Laboratories, Tiangong University, Queen's University, Xiamen University Status: Accepted/Under Review (Elsevier)

Overview

PacketPatch is a practical scheme for generating adversarial network packets against Byte-feature-based Encrypted Traffic Classification (B-ETC) models, aiming to protect user privacy. Unlike existing methods that rely on unrealistic white-box assumptions or introduce excessive overhead, PacketPatch operates under strict black-box conditions while maintaining real-time performance and packet usability.

Key Features

• Black-Box Operation: No access to target model gradients, parameters, or training data required
• User-Agnostic: Perturbation generation relies solely on current packet features, without user behavior history
• Real-Time Efficiency: Single forward pass generation, average latency ~10 ms per packet
• Controllable Bandwidth Overhead: Perturbation length ≤ 10% of original packet size
• Packet Validity & Recovability: Symmetric proxy architecture ensures packets pass integrity checks and can be perfectly restored at the receiver

Architecture

PatchGenerator Workflow

1. Input Preparation: Separate packet header from payload; prepare [MASK] tokens (length = 10% of packet size)
2. Sequence Construction: Assemble [CLS] + header + Pad1 + [SEP] + random_header(hr) + Pad2 + payload
3. Perturbation Generation: Single BERT forward pass → probability distributions over vocabulary
   • Pad1 → arg min (maximally disrupts original features)
   • Pad2 → arg max (generates features aligned with random header)
4. Vector Assembly: Final perturbation v = Pad1 + hr + Pad2

Repository Structure

PacketPatch/
├── README.md                          # Project overview (this file)
├── LICENSE                            # MIT License
├── requirements.txt                   # Python dependencies
├── bert_base_config.json              # BERT model configuration (12 layers, 768-dim, 12 heads)
│
├── packet_adv/                        # ★ Core experimental code
│   ├── README.md                      # Usage guide for core modules
│   ├── adv_fine_tuning_cls.py         # Step 1: Train PatchGenerator (SpanBERT)
│   ├── generate.py                    # Step 2: Generate adversarial packets
│   └── gen_onnx.py                    # Step 3 (optional): ONNX/TensorRT acceleration
│
├── uer/                               # UER (Universal Encoder Representations) framework
│   ├── README.md                      # Framework documentation
│   ├── encoders/                      # Encoder implementations (Transformer, RNN, CNN)
│   ├── layers/                        # Network layers (Embedding, Attention, FFN)
│   ├── targets/                       # Training targets (MLM, NSP, Span-MBM, etc.)
│   ├── models/                        # Model definition
│   ├── utils/                         # Utilities (tokenizers, optimizers, data loading)
│   ├── model_builder.py               # Model construction
│   ├── model_loader.py                # Pretrained weight loading
│   ├── model_saver.py                 # Model checkpoint saving
│   ├── trainer.py                     # Training loop implementation
│   └── opts.py                        # CLI argument definitions
│
├── dataset/                           # Dataset documentation
│   └── README.md                      # Dataset descriptions, download links, preprocessing
│
├── image/                             # Architecture and result figures
│   └── README.md                      # Figure descriptions
│
└── tools/                             # Utility tools
    └── README.md                      # Tool descriptions and usage

Quick Start

1. Environment Setup

# Clone this repository
git clone https://github.com/your-org/PacketPatch.git
cd PacketPatch

# Install dependencies
pip install -r requirements.txt

Requirements:

• Python 3.7+
• NVIDIA GPU with ≥6GB VRAM (recommended: RTX 3080 or above)
• 32GB+ system memory

2. Prepare Pretrained Model & Datasets

PacketPatch is built upon ET-BERT. Download the following from the ET-BERT repository:

• pretrained_model.bin — ET-BERT pretrained weights
• encryptd_vocab.txt — Vocabulary file for byte-pair encoding

Datasets: We evaluate on three public datasets. See dataset/README.md for download links and preprocessing instructions.

Dataset	Classes	Scenario
ISCX-TOR	16	Tor anonymous traffic
ISCX-VPN	11	VPN encrypted tunnels
USTC	20	Malware + benign traffic

3. Train PatchGenerator

cd packet_adv

# Edit adv_fine_tuning_cls.py first:
#   - Update dataset paths (lines 47-50)
#   - Update pretrained model path (lines 73-75)
#   - Set correct num_classes (line 56)
#   - Set correct training set size (line 878)

python adv_fine_tuning_cls.py

The trained checkpoint will be saved in the specified log directory (e.g., span_2seg_cls_log/).

4. Generate Adversarial Packets

cd packet_adv

# Edit generate.py first:
#   - Update test data paths (lines 42-44)
#   - Update --load_model_path to your trained checkpoint (line 68-69)
#   - Set BWO (bandwidth overhead ratio, line 60, default 0.1)
#   - Choose generation strategy via 'fun' parameter (line 50)

python generate.py

5. (Optional) Model Acceleration with TensorRT

cd packet_adv

# Edit gen_onnx.py first:
#   - Update model checkpoint path (line 132)
#   - Update output paths (lines 145, 207)
#   - Select precision mode: 'fp16' or 'int8' (line 21)

python gen_onnx.py

Key Configuration Parameters

Parameter	File	Line	Description	Default
BWO	generate.py	60	Bandwidth overhead ratio	0.1 (10%)
fun	generate.py	50	Generation strategy	'fun5'
n_epochs	adv_fine_tuning_cls.py	53	Training epochs	50
batch_size	adv_fine_tuning_cls.py	54	Batch size	32
num_classes	adv_fine_tuning_cls.py	56	Number of traffic classes	Dataset-dependent
learning_rate	adv_fine_tuning_cls.py	94-95	Learning rate	2e-5
seq_length	adv_fine_tuning_cls.py	92-93	Input sequence length	256

Generation Strategies (fun parameter)