Packet2Vec requires Python 3.5 or greater. The required libraries can be found in requirements.txt.

pip install -r requirements.txt

To compile the required shared library (ParallelPcap), Packet2Vec requires the following software:

• CMake
• Boost >=1.66.0

Notes on building Boost from scratch can be found here.

To compile ParallelPcap:

cd ParallelPcap
mkdir build
cd build
cmake ..
make parallelpcap

The shared library must be placed in the same directory as main.py:

mv bin/parallelpcap.so ../..

A complete Packet2Vec run uses a YAML file that specifies all the information required for generating features and training. The following YAML file is an example of one used for a complete run. Note that all text following # is a comment and is not necessary for the file to run. A sample configuration file is also provided in the repository.

working: /path/to/working/directory          
train_data: /path/to/raw/training/data
test_data: /path/to/raw/testing/data
embeddings: /path/to/trained/embeddings/model
darpa: /path/to/groundtruth/csv/file
options:
  threads: 10
hyperparameters:
  ngram: 2
  vocab_size: 50000
classifiers:
  - rfc
  - gnb

python3 main.py run -c packet2vec_config.yml

Packet2Vec requires the user to specify several directories and files:

• working: Working directory. Packet2Vec will use this directory to store temporary files. Currently, this directory also acts as the output directory.
• train_data: Directory containing raw pcap files for training.
• test_data: Directory containing raw pcap files for testing.
• embeddings: Optional. Path to a saved Word2Vec embeddings model. If this option is present in the YAML file, Packet2Vec will update a saved Word2Vec model rather than training a new one.
• darpa: Path to the groundtruth file for the DARPA2009 dataset.

Packet2Vec includes several optional user-definable parameters that can be specified in the YAML configuration file:

• threads: Number of processors to use to speed up ParallelPcap. Default is 1.

Packet2Vec includes several optional user-definable hyperparameters for the dictionary that can be specified in the YAML configuration file:

• ngram: The size of the ngrams ParallelPcap will compute.
• vocab_size: The size of the vocabulary for the ParallelPcap dictionary.

Packet2Vec is configured to perform binary classification using two classifiers. Extending this code to add more classifiers should be relatively straightforward.

• rfc: Random Forest Classifier.
• gnb: Naive Bayes Classifier.

Packet2Vec allows the user to run any step in the process individually:

• tokens: The tokens mode will only generate a dictionary and integer representations of the raw pcap files.

python3 main.py tokens -c packet2vec_config.yml

• embeddings: The embeddings mode will only train the Word2Vec model. This requires the token vectors generated in the tokens step to be present in the working directory.

python3 main.py embeddings -c packet2vec_config.yml

• features: The features mode will use the trained Word2Vec model to generate embedding-based feature vectors. This mode requires that the integer representations and a saved embeddings model are present in the working directory.

python3 main.py features -c packet2vec_config.yml

• classifiers: The classifiers mode will train and test the binary classifiers on the generated feature vectors. This mode requires that the feature vectors generated in the features step are present in the working directory.

python3 main.py classifiers -c packet2vec_config.yml