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ACOLYTE (dAta Curation fOr traceabiLity sYsTEm)

ACOLYTE is a Python application designed to facilitate traceability in autonomous systems in a cibersecure manner. It has been developed to assist in the automated extraction of data from autonomous systems. The application is available in the form of a command-line tool. Two fundamental options are available:

  • Store records from a rosbag2 file.
  • Get stored records by a timeframe.

This Python application is dependant on BCubed Python library. BCubed is available at BCubed repository.

⚠️ Disclaimer

This Python application has only been tested in simulated blockchain environments. Using it in other environments is at your own risk. Keep possible charges in mind.

Getting Started

Create and activate a virtual enviroment (recommended)

$ python3 -m venv .venv
$ source .venv/bin/activate

Install the requirements

(.venv) pip install -r requirements.txt

Install BCubed Python library

Follow the instructions available in BCubed README.md. It is important to note that the virtual environment has already been created.

Install ACOLYTE

(.venv) $ pip install -e <acolyte_location>

Execute ACOLYTE

  1. Ensure the blockchain network is available.

    • (Optional) Execute an Ethereum simulator.
  2. Update the configuration data in the config.yaml file.

  3. Update the configuration data in the topics-config.yaml file.

Store records that are created by reading topic messages from a rosbag2 file

(.venv) $ acolyte -a store -i <rosbag2_file> -r <responsible> -w rosbag

Store records that are created by reading topic messages from a rosbag2 file and monitoring the operating system

(.venv) $ acolyte -a store -i <rosbag2_file> -r <responsible> -w rosbag -os

Get stored records by a timeframe

(.venv) $ acolyte -a get_by_timestamp -ts <timestamp_start> -te <timestamp_end>

Uninstall ACOLYTE

(.venv) $ pip uninstall acolyte

Dataset available

A dataset has been created to facilitate testing of the application without the need to create your own rosbag. It is available at the following URL: https://zenodo.org/records/16630061.

Acknowledgements

This research is part of the project TESCAC, financed by “European Union NextGeneration-EU, the Recovery Plan, Transformation and Resilience, through INCIBE".

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