CyberShu

CyberShu is an open-source project that combines the systematic problem-solving methodology of TRIZ with AI to analyze cybersecurity patents. By automating the analysis of contradictions, resolutions, and trends, CyberShu aims to uncover hidden insights in the realm of cybersecurity threat detection.

Features

TRIZ-Driven Patent Analysis: Leverages the principles of TRIZ to extract contradictions and map inventive solutions.
AI-Powered Insights: Uses advanced natural language processing (NLP) and machine learning models to analyze large patent datasets.
Cybersecurity Focus: Tailored specifically to uncover trends, gaps, and opportunities in threat detection innovation.
Visualization: Generates intuitive visualizations for trends, contradictions, and interdisciplinary insights.

Goals

Automate Altshuller’s TRIZ methodology to analyze patents at scale.
Uncover new patterns and contradictions that humans may overlook.
Inspire innovation in cybersecurity by identifying gaps and novel opportunities.

Repository Structure

data/: Contains raw and processed patent datasets.
models/: AI models for NLP, clustering, and trend analysis.
notebooks/: Jupyter Notebooks for experiments and demonstrations.
scripts/: Python scripts for preprocessing, analysis, and visualization.
results/: Outputs such as visualizations and reports.
docs/: Documentation for users, contributors, and license details.

Getting Started

Prerequisites

Python 3.10+

Required Libraries:

pip install pandas numpy nltk spacy gensim transformers faiss-cpu bertopic matplotlib plotly scikit-learn

Setup

Clone the repository:

git clone https://github.com/yourusername/CyberShu.git
cd CyberShu

Set up the environment:

python -m venv env
source env/bin/activate   # On Windows: env\Scripts\activate
pip install -r requirements.txt

Prepare datasets:
- Place raw patent files in the data/raw/ directory.

Run example notebooks or scripts:

python scripts/preprocess.py
python scripts/analyze.py

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License. See LICENSE.md for details.

Contact

For questions or collaboration:

Email: bparanj@zepho.com
GitHub Issues: CyberShu Issues

Acknowledgments

Inspired by Genrich Altshuller’s TRIZ methodology.
Special thanks to the open-source community for tools and datasets that power this project.

License

bparanj/patents

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