There’s Always a Bigger Fish 🎣

Sabancı University Bachelor's Thesis 2024

Team: Ali Parlakçı, Doğukan Yıldırım, Rana İşlek

Code from the paper “There’s Always a Bigger Fish: A Clarifying Analysis of a Machine-Learning-Assisted Side-Channel Attack”.

You can reach our paper through this link.

Abstract

A loop-counting attack increments the value of a counter during a short period of time, repeats the experiments multiple times, and analyzes the sequence of counter values observed, which are affected by the system interrupts, to infer secret information about a victim. These attacks have been so far leveraged for website fingerprinting. In this work, we show that the same attack can also be used for GPU (graphics processing unit) application fingerprinting. More specifically, we demonstrate that the GPU applications typically cause distinguishable patterns of interrupts between the GPU and the CPU, affecting the counter values, such that the sequence of counter values can be analyzed to determine the application running on the GPU. In our experiments conducted on various combinations of operating systems (OS), browsers, media players, and video codecs, we observed that the proposed approach correctly predicted the media player running on the GPU with an average accuracy of 87.99% and the codec of the video being played with an average accuracy of 62.92%

Setup

Rana is here and Ali is happy.

Install dependencies

Make sure you have Python 3 installed. Then, install our dependencies.

pip install -r requirements.txt

Install browser drivers

You only need to install the drivers for the browsers you would like to use.

• Chrome: Download here and add chromedriver to your path
• Firefox: Download here and add geckodriver to your path
• Safari: Skip this step! safaridriver is built into macOS
• Tor Browser: Install tor-browser-selenium
• Links: We didn’t keep any experiments with this text-based browser for the paper, but on macOS you can install it with brew install links and use our built-in driver

Usage

Our recording script takes many command-line arguments, making a wide variety of experiments possible. Most experiments we ran in the paper should be covered here, and you can also create your own experiments by modifying the arguments or changing the attacker code in attacker/worker.js.

1. Basic recording setup (Table 1)
   1. Adding countermeasures (Table 2)
   2. Tor Browser experiments
   3. Using older versions of Chrome
   4. Setting up SMS notifications
2. Isolation experiments (Table 3)
   1. Pin attacker and victim to separate cores
   2. Isolate interrupts from attacker core
3. eBPF analysis tool (Figure 5)
4. Changing timer parameters (Table 4)

Evaluation

Once you’ve collected some data, you’ll want to train a model and test it to find its accuracy. For small experiments, we’ve included a script, check_results.py, that trains a simple random forest model for a quick sanity check.

# Record 40 5-second traces of the top 4 websites according to Alexa, and
# save the traces to readme-experiment
$ python record_data.py --num_runs 40 --trace_length 5 --sites_list alexa4 --out_directory readme-experiment
...
100%|█████████████████████████████████████████| 160/160 [14:17<00:00,  5.36s/it]

# Load the traces and check accuracy
$ python scripts/check_results.py --data_file readme-experiment
Analyzing results from readme-experiment
100%|███████████████████████████████████████████| 10/10 [00:01<00:00,  5.83it/s]

Number of traces: 160

top1 accuracy: 87.3% (+/- 6.8%)
top5 accuracy: 100.0% (+/- 0.0%)

For larger experiments, you’ll want to train an LSTM, as we do in the paper. For ease of use, we’ve included our training code in a Colab notebook: https://colab.research.google.com/drive/1GRQwuxlfoCPaiM7BiP9giHS2sMppvYHH?usp=sharing.

FAQs

How should I cite this work?

Please use the following BibTeX entry:

@inproceedings{cook2022biggerfish,
    author = {Cook, Jack and Drean, Jules and Behrens, Jonathan and Yan, Mengjia},
    title = {There's Always a Bigger Fish: A Clarifying Analysis of a Machine-Learning-Assisted Side-Channel Attack},
    year = {2022},
    publisher = {Association for Computing Machinery},
    url = {https://doi.org/10.1145/3470496.3527416},
    doi = {10.1145/3470496.3527416},
    booktitle = {Proceedings of the 49th Annual International Symposium on Computer Architecture},
    pages = {204–217}
}

License

This repository is available under the MIT license. See the LICENSE file for more details.