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A python app to predict Att&ck tactics and techniques from cyber threat reports

License: MIT License

Python 78.63% HTML 21.37%
attack cyber-threat-intelligence multi-label-classification ttp

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rcatt's Issues

BUG: Sklearn is now deprecated.

When I try to run rcATT_gui.py, it runs fine. When I access the website, put some dummy text inside and press "Predict", the whole thing crashes and displays "ModuleNotFoundError: No module named 'sklearn.feature_selection.univariate_selection'", as seen in the screenshots below.
image
Is there anything that can be done?

data source

Where did you get your dataset to train the model?

Just labeled data from
https://github.com/mitre/cti ?
I see there are 1700 records in total, which is not enough to train a machine learning model I suppose.

Or have you ever thought about collecting threat reports from other sites and human label them?

Missing requirements

This is a basic requirements.txt just in case you want to add it for an easier installation via pip.

colorama == 0.4.3
flask == 1.1.2
joblib == 0.14.1
nltk == 3.4.5
numpy == 1.17.4
pandas == 0.25.3
sklearn <= 0.22
stix2 == 1.2.1

Added data CSV file not used in training

I noticed that when running the train command, the "added data" CSV file classification_tools/data/training_data_added.csv is not actually appended to the "original" training dataset:

train_data_df.append(train_data_added, ignore_index = True)

Note that the Pandas DataFrame.append function returns a new DataFrame and does not modify its input, so the corrected version would be:

	train_data_df = train_data_df.append(train_data_added, ignore_index = True)

I'd be happy to submit this as a merge request, if that'd work.

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