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In this repository, we guide you in setting up the TrafficGPT project in a local environment and reproducing the results. TrafficGPT, a novel traffic analysis attack that leverages GPT-2, a popular LLM, to enhance feature extraction, thereby improving the open-set performance of downstream classification. We use five existing encrypted traffic datasets to show how the feature extraction by GPT-2 improves the open-set performance of traffic analysis attacks. As the open-set classification methods, we use K-LND, OpenMax, and Backgroundclass methods, and shows that K-LND methods have higher performance overall.
Datasets: AWF, DF, DC, USTC, CSTNet-tls
Openset methods
- K-LND methods
- OpenMax
- Background class
First, clone the git repo and install the requirements.
git clone https://github.com/YasodGinige/TrafficGPT.git
cd TrafficGPT
pip install -r requirements.txt
Next, download the dataset and place it in the data directory.
gdown https://drive.google.com/uc?id=1-MVfxyHdQeUguBmYrIIw1jhMVSqxXQgO
unzip data.zip
Then, preprocess the dataset you want to train and evaluate. Here, the dataset name should be DF, AWF, DC, USTC, or CSTNet.
python3 data_preprocess.py --data_path ./data --dataset <dataset_name>
To train the model, run the suitable code for the dataset:
python3 train.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 60 --dataset DF
python3 train.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 200 --dataset AWF
python3 train.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 4 --dataset DC
python3 train.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 12 --dataset USTC
python3 train.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 75 --dataset CSTNet
To evaluate, run the suitable code for the dataset:
python3 evaluate.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 60 --K_number 30 --TH_value 0.8 --dataset DF
python3 evaluate.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 200 --K_number 50 --TH_value 0.9 --dataset AWF
python3 evaluate.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 4 --K_number 4 --TH_value 0.9 --dataset DC
python3 evaluate.py --max_len 1024 --batch_size 12 --epochs 3 --num_labels 12 --K_number 5 --TH_value 0.8 --dataset USTC
python3 evaluate.py --max_len 1024 --batch_size 12 --epochs 5 --num_labels 75 --K_number 20 --TH_value 0.8 --dataset CSTNe