Welcome to Trustee’s documentation. Get started with installation and then get an overview with the quickstart. The rest of the docs describe each component of Trustee in detail, with a full reference in the API section.

Overview#

Trustee is a framework to extract decision tree explanation from black-box ML models.

Trustee Flowchart — Standard AI/ML development pipeline extended by Trustee.#

Getting Started#

This section contains basic information and instructions to get started with Trustee.

Python Version#

Trustee supports Python >=3.7.

Install Trustee#

Use the following command to install Trustee:

$ pip install trustee

Sample Code#

from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

from trustee import ClassificationTrustee

X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)

clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

trustee = ClassificationTrustee(expert=clf)
trustee.fit(X_train, y_train, num_iter=50, num_stability_iter=10, samples_size=0.3, verbose=True)
dt, pruned_dt, agreement, reward = trustee.explain()
dt_y_pred = dt.predict(X_test)

print("Model explanation global fidelity report:")
print(classification_report(y_pred, dt_y_pred))
print("Model explanation score report:")
print(classification_report(y_test, dt_y_pred))

Other Use Cases#

For other examples and use cases of how Trustee can used to scrutinize ML models, listed in the table below, please check our Use Cases repository.

Use Case	Description
heartbleed_case/	Trustee application to a Random Forest Classifier for an Intrustion Detection System, trained with CIC-IDS-2017 dataset pre-computed features.
kitsune_case/	Trustee application to Kitsune model for anomaly detection in network traffic, trained with features extracted from Kitsune’s Mirai attack trace.
iot_case/	Trustee application to Random Forest Classifier to distguish IoT devices, trained with features extracted from the pcaps from the UNSW IoT Dataset.
moon_star_case/	Trustee application to Neural Network Moon and Stars Shortcut learning toy example.
nprint_ids_case/	Trustee application to the nPrintML AutoGluon Tabular Predictor for an Intrustion Detection System, also trained using pcaps from the CIC-IDS-2017 dataset.
nprint_os_case/	Trustee application to the nPrintML AutoGluon Tabular Predictor for OS Fingerprinting, also trained using with pcaps from the CIC-IDS-2017 dataset.
pensieve_case/	Trustee application to the Pensieve RL model for adaptive bit-rate prediction, and comparison to related work Metis.
vpn_case/	Trustee application the 1D-CNN trained to detect VPN traffic trained with the ISCX VPN-nonVPN dataset.

Supported AI/ML Libraries#

Library	Supported
scikit-learn	✅
Keras	✅
Tensorflow	✅
PyTorch	✅
AutoGluon	✅

API Reference#

If you are looking for information on a specific function, class or method, this part of the documentation is for you.

API
- Trustee
- Trust Report
Examples

Citing Us#

@inproceedings{Jacobs2022,
  title        = {AI/ML and Network Security: The Emperor has no Clothes},
  author       = {A. S. Jacobs and R. Beltiukov and W. Willinger and R. A. Ferreira and A. Gupta and L. Z. Granville},
  year         = 2022,
  booktitle    = {Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security},
  location     = {Los Angeles, CA, USA},
  publisher    = {Association for Computing Machinery},
  address      = {New York, NY, USA},
  series       = {CCS '22}
}