Laserbeak: Evolving Website Fingerprinting Attacks with Attention and Multi-Channel Feature Representation

An improved website fingerprinting classification model that achieves state-of-the-art against defended network traffic

LASERBEAK is a state-of-the-art website fingerprinting (WF) attack model that achieves significant performance improvements against defended Tor traffic. By integrating multi-channel traffic representations and advanced techniques adapted from computer vision models, LASERBEAK pushes the boundaries of WF attack performance. Key innovations include:

• Multi-Channel Feature Representation: Provides a richer context for the model by leveraging various traffic features, improving robustness against defenses.
• Advanced Transformer-Based Architecture: Utilizes multi-headed attention layers to capture both local and global patterns in the traffic data.

In evaluations, LASERBEAK outperforms existing WF attacks, demonstrating up to 36.2% performance improvements in defended scenarios.

Key Contributions

1. Multi-Channel Traffic Representations: Combining multiple traffic features leads to significant accuracy improvements against defended samples.
2. Novel WF Model: Leveraging state-of-the-art transformer-based techniques to enhance WF attack performance.
3. Precision Optimizers: Introducing new methods for improving precision in open-world scenarios, critical for realistic attacks.
4. Comprehensive Dataset Evaluation: Extensive testing on both closed-world and open-world datasets to validate the model's efficacy.

Usage

Training a LASERBEAK Model

To train a LASERBEAK model on a specified dataset, use the following command:

python benchmark.py \
    --data_dir /path/to/data \
    --ckpt_dir /path/to/checkpoints \
    --results_dir /path/to/results \
    --config ./configs/laserbeak.json \
    --dataset be-front \
    --epochs 20 \
    --multisamples 10 \
    --exp_name my_experiment

This command trains the model on the be-front dataset using x10 simulated samples for 20 epochs.

Evaluation

To evaluate a pre-trained LASERBEAK model, use:

python benchmark.py \
    --data_dir /path/to/data \
    --ckpt /path/to/model_checkpoint.pth \
    --eval --dataset be-front

This command loads the model checkpoint and evaluates its performance on the be-front dataset.

Available Datasets

The benchmark.py script supports various datasets for training and evaluation, including:

• be: Basic dataset
• be-front: Front-defended dataset
• amazon: Amazon traffic dataset
• webmd: WebMD traffic dataset
• gong: Gong dataset with Surakav defenses

Refer to the --dataset option in the command-line arguments for the full list of available datasets.

We provide these datasets as pre-packaged pickle files that can be downloaded from the this Google drive directory. To load the provided files using the benchmark.py script, the --data_dir argument should be used to direct to the parent directory containing the wf-* subdirectories. The file and subdirectory names must be the same as provided in the Google drive to function correctly.

Warnings

This code is research-grade and may contain bugs or other issues. Users are advised to carefully validate results and use the code at their own risk.