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Pytorch implementation of deep packet: a novel approach for encrypted traffic classification using deep learning
| Date | Stars |
|---|---|
| 2026-07-31 | 253 |
| 2026-08-03 | 253 |
| 2026-08-06 | 253 |
Today
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Momentum
0.0
growth rate 0.00%/day
# Deep Packet
Details in blog
post: https://blog.munhou.com/2020/04/05/Pytorch-Implementation-of-Deep-Packet-A-Novel-Approach-For-Encrypted-Tra%EF%AC%83c-Classi%EF%AC%81cation-Using-Deep-Learning/
## Changelog
### EDIT: 2022-11-30
* Add the ResNet model. Kudos to [Taehyun](https://github.com/k1m743hyun) for implementing ResNet.
### EDIT: 2022-09-27
* Update dataset and model
* Update dependencies
* Add more data to `chat`, `file_transfer`, `voip`, `streaming` and `vpn_voip`
* Remove tor and torrent related data as they are no longer available
### EDIT: 2022-01-18
* Update dataset and model
### EDIT: 2022-01-17
* Update code and model
* Drop `petastorm`, use huggingface's `datasets` instead for data loader
## How to Use
* Clone the project
* Create environment via conda
* For Mac
```bash
conda env create -f env_mac.yaml
```
* For Linux (CPU only)
```bash
conda env create -f env_linux_cpu.yaml
```
* For Linux (CUDA 10.2)
```bash
conda env create -f env_linux_cuda102.yaml
```
* For Linux (CUDA 11.3)
```bash
conda env create -f env_linux_cuda113.yaml
```
* Download the train and test set I created
at [here](https://drive.google.com/file/d/1EF2MYyxMOWppCUXlte8lopkytMyiuQu_/view?usp=sharing), or download
the [full dataset](https://www.unb.ca/cic/datasets/vpn.html) if you want to process the data from scratch.
## Data Pre-processing
```bash
python preprocessing.py -s /path/to/CompletePcap/ -t processed_data
```
## Create Train and Test
```bash
python create_train_test_set.py -s processed_data -t train_test_data
```
## Train Model
Application Classification
For CNN model
```bash
python train_cnn.py -d train_test_data/application_classification/train.parquet -m model/application_classification.cnn.model -t app
```
For Resnet model
```bash
python train_resnet.py -d train_test_data/application_classification/train.parquet -m model/application_classification.cnn.model -t app
```
Traffic Classification
For CNN model
```bash
python train_cnn.py -d train_test_data/traffic_classification/train.parquet -m model/traffic_classification.cnn.model -t traffic
```
For Resnet model
```bash
python train_resnet.py -d train_test_data/traffic_classification/train.parquet -m model/traffic_classification.cnn.model -t traffic
```
## Evaluation Result (CNN)
### Application Classification
### Traffic Classification
## Model Files
Download the pre-trained CNN
models [here](https://drive.google.com/file/d/1LFrx2us11cNqIDm_yWcfMES5ypvAgpmC/view?usp=sharing).
## Elapsed Time
### Preprocessing
Code ran on AWS `c5.4xlarge`
```
7:01:32 elapsed
```
### Train and Test Creation
Code ran on AWS `c5.4xlarge`
```
2:55:46 elapsed
```
### Traffic Classification Model Training (CNN)
Code ran on AWS `g5.xlarge`
```
24:41 elapsed
```
### Application Classification Model Training (CNN)
Code ran on AWS `g5.xlarge`
```
7:55 elapsed
```
Excerpt of 3,126 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:a404589ec6c1cbe7, topic:deep-learning, topic:pytorch