Technical requirements
To successfully implement AI-driven security solutions with Python, several technical components and tools are necessary:
- Python environment:
- Python version: Python 3.6 or higher to ensure compatibility with modern AI libraries.
- Virtual environment: Set up a virtual environment using
venvorvirtualenvto manage dependencies.
- AI libraries and frameworks:
- TensorFlow or PyTorch: For building and training ML and deep learning (DL) models.
- scikit-learn: For implementing traditional ML algorithms and model evaluation.
- Keras: A high-level neural network (NN) API running on top of TensorFlow for easier model building.
- pandas and NumPy: For data manipulation and numerical operations.
- Data sources:
- Security data: Access to relevant security data such as logs, alerts, and network traffic. This can include data from security information and event management (SIEM) systems, intrusion detection systems (IDSs), or threat intelligence (TI) feeds.
- Data preprocessing...
