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MAGIC

This is official code for the USENIX Security 24 paper:

MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning

In this paper, we introduce MAGIC, a novel and flexible self-supervised approach for multi-granularity APT detection. MAGIC leverages masked graph representation learning to model benign system entities and behaviors, performing efficient deep feature extraction and structure abstraction on provenance graphs. By ferreting out anomalous system behaviors via outlier detection methods, MAGIC is able to perform both system entity level and batched log level detection. MAGIC is specially designed to handle concept drift with a model adaption mechanism and successfully applies to universal conditions and detection scenarios.

Dependencies

  • Python 3.8
  • PyTorch 1.12.1
  • DGL 1.0.0
  • Scikit-learn 1.2.2

Datasets

We use two public datasets for evaluation on batched log level detection: StreamSpot and Unicorn Wget. We use the DARPA Transparent Computing Engagement 3 sub-datasets E3-Trace, E3-THEIA and E3-CADETS for evaluation on system entity level detection. Due to the enormous size of these datasets, we include our pre-processed datasets in the data/ folder. In each sub-directory under the .data folder, there is a .zip file. You need to unzip these .zip files into one graphs.pkl for each dataset.

To pre-process these datasets from scratch, do as the follows:

  • StreamSpot Dataset
    • Download and unzip all.tar.gz from StreamSpot, which includes a single data file all.tsv. Copy all.tsv to data/streamspot.
    • Go to directory utils and run streamspot_parser.py. This will result in 600 graph data files in the JSON format.
    • During training and evaluation, function load_batch_level_dataset in utils/loaddata.py will automatically read and label these graphs and store them into the compressed data archive graphs.pkl for efficient data loading.
  • Unicorn Wget Dataset
    • Download and unzip attack_baseline.tar.gz and benign.tar.gz from Wget. Copy all .log files into data/wget/raw/. Ignore contents in base and stream.
    • Go to directory utils and run wget_parser.py. This will result in 150 graph data files in the JSON format.
    • During training and evaluation, function load_batch_level_dataset in utils/loaddata.py will automatically read and label these graphs and store them into the compressed data archive graphs.pkl for efficient data loading.
  • DARPA TC E3 Sub-datasets
    • Go to DAPRA TC Engagement 3 data release.
    • Download and unzip ta1-trace-e3-official-1.json.tar.gz into data/trace/.
    • Download and unzip ta1-theia-e3-official-6r.json.tar.gz into data/theia/.
    • Download and unzip ta1-cadets-e3-official-2.json.tar.gz and ta1-cadets-e3-official.json.tar.gz into data/cadets/.
    • Do not delete log files that are not directly used for training and test purpose (e.g. ta1-theia-e3-official-6r.4-7.json). These files provide entity definitions for subsequent event records, including definitions for malicious entities.
    • Go to directory utils and run trace_parser.py with argument --dataset. Valid choices are trace, theia, and cadets.
    • MAGIC is evaluated on the DARPA TC datasets using the ThreaTrace label. Go to ThreaTrace, download the .txt groundtruth files from the folder "groundtruth" and put them into the corresponding dataset folder of MAGIC. For example, theia.txt into data/theia/theia.txt.

Meanwhile, we elaborated an alternative labeling methodology on the DARPA TC datasets in our paper(Appendix G). We also provided the corresponding ground truth labels in the same appendix section for sub-datasets E3-Trace, E3-THEIA and E3-CADETS.

Run

This is a guildline on reproducing MAGIC's evaluations. There are three options: Quick Evaluation, Standard Evaluation and Training from Scratch.

Quick Evaluation

Make sure you have MAGIC's parameters saved in checkpoints/ and KNN distances saved in eval_result/. Then execute eval.py and assign the evaluation dataset using the following command:

  python eval.py --dataset *your_dataset*

Standard Evaluation

Standard evaluation trains the detection module from scratch, so the KNN distances saved in eval_result/ need to be removed. MAGIC's parameters in checkpoints/ are still needed. Execute eval.py with the same command to run standard evaluation:

  python eval.py --dataset *your_dataset*

Training from Scratch

Namely, everything, including MAGIC's graph representation module and its detection module, are going to be trained from raw data. Remove model parameters from checkpoints/ and saved KNN distances from eval_result/ and execute train.py to train the graph representation module.

  python train.py --dataset *your_dataset*

Then execute eval.py the same as in standard evaluation:

  python eval.py --dataset *your_dataset*

For more running options, please refer to utils/config.py

Cite

If you make advantage of MAGIC in your research, please cite the following in your manuscript:

@inproceedings{jia2024magic,
  title        = {MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learning},
  author       = {Zian Jia and
                  Yun Xiong and
                  Yuhong Nan and
                  Yao Zhang and
                  Jinjing Zhao and
                  Mi Wen},
  booktitle    = {33rd USENIX Security Symposium, USENIX Security 2024},
  year         = {2024},
}