This repository contains implementation fotr the paper "Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTI".
Run pip install -r requirements.txt on a virtual environment
Open the notebook in notebooks/attack-pattern-extraction.ipynb in Google colab for demo on attack pattern extraction and mapping from CTI texts.
Open the notebook in notebooks/malware-similarity.ipynb in Google colab for malware similarity analysis from all triples
Change directory to attack_pattern
Run pip install -r requirements.txt to install the dependencies in a virtual environment
To train the sentence classification model: python finetune_sentence_classification.py --save-path=logs/sentence_classification
Edit the params_dict values in the python file to fine tune other combination of hyperparameters.
To train the entity extraction model: python finetune_entity_extraction.py --save-path=logs/entity_extraction
Edit the params_dict values in the python file to fine tune other combination of hyperparameters.
Change directory to ner
Run pip install -r requirements.txt to install the dependencies in a virtual environment
To train NER model run: python train_ner.py
cfgEdit cfg values in the python file to fine tune other combination of hyperparameters.
infer.py shows examples on how to create annotation in BRAT format with trained model.
Change directory to relation_extraction
Run pip install -r requirements.txt to install the dependencies in a virtual environment
To train Relation Extraction model run: python train_supervised.py
Run the notebook train_class_specific.ipynb to get prediction result for different classes
Run the notebook tucker_malkg_usecase.ipynb to get atack pattern prediction for FluBot
Text file and corresponding BRAT annotations are provided in data/1-150 directory.