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EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis

Data is available at DOI

  • EMBER metadata with AVClass2 re-run
  • AVClass2 tag co-occurrence results
  • XGBoost leaf similarity results

Usage

First, refer to EMBER repo (https://github.com/elastic/ember) for instructions on how to obtain train & test datasets, as well as how to run feature extraction.

Train the model

With src/xgboost_trainer.py you can:

  • perform a grid search to find the best hyperparameters values for the xgboost model
  • train an xgboost model with the hyperparameters values recommended by us
  • train an xgboost model with your own hyperparameters values Check the source for details about the required arguments.

Compute the leaf predictions

With src/leaf_similarity/leaf_pred_predictions.py you can:

  • generate the leaf predictions dataset for train and test starting from an xgboost model and the ember dataset
  • generate the prediction scores for the unlabelled subset from the EMBER dataset Check the source for details about the required arguments.

Get the top 100 most similar entries for any sample

This is a time consuming task. As so, we divided the process into two steps:

  1. With src/leaf_similarity/leaf_pred_top_100_search.py you can:
  • generate the top 100 similar hits for different query vs knowledge base combinations, such as: test vs train + test, unlabelled vs train, unlabelled vs train + test
  • this will save the results in multiple pickle files Check the source for details about the required arguments.
  1. With src/leaf_similarity/leaf_pred_top_100_shas.py you can, starting from the results in the previous step:
  • generate a csv file where for a sha there will be 100 most similar other shas, depending on the targeted datasets
  • you can also specify if the similarity score should be present or not in the results Check the source for details about the required arguments.

Get similarity search statistics

You can compute the statistics of the similarity search both from the binary labels and multiclass labels.

  • With src/leaf_similarity/leaf_pred_binary_stats.py, you can get statistics of the leaf similarity search based on the benign / malicious labels
  • With src/leaf_similarity/leaf_pred_class_stats.py, you can get statistics of the leaf similarity search based on the class presented in the EMBER metadata Check the source for details about the required arguments.

Running AVClass

First, ensure you cloned the AVClass repo (https://github.com/malicialab/avclass).

Given a JSONL input file (one JSON object per line) with VirusTotal detection results, you can use the src/run_avclass.sh script to run the AVClass' labeler for obtaining sample tags.

Tag-related operations (e.g. augmentation via co-occurrence, ranking etc.) are defined in src/dataset.py.

Parse AVClass results and add to EMBER dataframe

You can parse the AVClass results and augment a dataframe with original EMBER metadata by using src/parse_avclass.py. Example:

python3 parse_avclass.py \
  --avclass-results-file avclass_results.txt \
  --ember-dataframe-csv ember_original_metadata.csv \
  --output-dataframe-path ember_with_avclass_dataset.csv

This dataset is already provided, see DOI at the beginning of this README.

Adding tags via co-occurrence

Given a dataframe with EMBER metadata, AVClass tag co-occurrence information (AVClass .alias file) and a co-occurrence threshold, you can use TagAugmenter from src/dataset.py to add extra tags to samples:

tag_assoc = TagAssociations("avclass_tag_co_occurrence.alias")
tag_aug = TagAugmenter(tag_assoc, thr_co_occur=0.9)
dataframe = pd.read_csv("ember_with_avclass_dataset.csv")
dataframe["EXTRA_TAGS"] = dataframe.apply(tag_aug.resolve_final_tags, axis=1)

Ranking tags

To obtain tag rankings (for FAM or CLASS tags, e.g. in order to prepare for evaluation), you can use get_tag_ranking from src/dataset.py:

dataframe = pd.read_csv("ember_with_avclass_dataset.csv")
# computing ranks requires having non-null AVClass tag info and co-occurrence info (from the step above)
dataframe["TAG_RANKS"] = dataframe.query("avclass_curr.notna() & EXTRA_TAGS.notna()").apply(
  lambda row: get_tag_ranking(
      tag_scores=row["avclass_curr"],
      co_occurrence=row["EXTRA"],
      tag_kind="FAM", # rank by FAM tags
      return_scores=False,
  ),
  axis=1,
)

Evaluation

For evaluating the XGBoost-based similarity search, you can start from the src/e2e.py script, which does the following:

  • loads the original EMBER dataset and constructs TAG_RANKS for either CLASS or FAM tags
    • for ranking tags using tag co-occurrence information, the .alias file is required, as constructed by AVClass
    • if you wish to not use tag co-occurrence information, there is an option to keep only the most prevalent tag (i.e. by AVClass rank score) as the ground truth for a sample
  • loads the similarity search results from a dataframe structured as: needle_sha256 -> [hits_sha256]
    • for info on how to generate top-N most similar samples for a given query, see above
  • computes relevance@K using a relevance function specified by the user: exact match, IOU (intersection over union), normalized edit similarity

After results are dumped to the pickle file, you can explore them using the notebooks/evaluation.ipynb notebook, which provides functionality for plotting histograms, empirical CDFs and constructing a summary table with descriptive statistics.

Evaluation metrics are implemented in src/evaluation.py.

References

Support statement

EMBERSim is an open source project, not a CrowdStrike product. As such, it carries no formal support, expressed or implied.