Day 1: Setting Up for AI-Driven Cybersecurity
Duration: 1 Hour
Objective: Equip participants with the necessary tools and understanding to start building AI models for cybersecurity.
Session Details:
- Tools and Technologies Overview: Introduction to the software and tools that will be used in the workshop (e.g., Python, TensorFlow, Keras).
- Data Handling for Security: Discussing the types of data needed for cybersecurity AI models and how to preprocess this data.
- Hands-On Activity: Installing the necessary software and libraries; getting familiar with the dataset that will be used for model training.
Day 2: Building AI Models for Threat Detection
Duration: 1 Hour
Objective: Develop skills to create and train machine learning models to detect cybersecurity threats.
Session Details:
- Feature Selection and Model Training: Techniques for selecting the right features from data to improve model accuracy.
- Anomaly Detection Models: Building and training models to detect unusual activities using supervised and unsupervised learning.
- Hands-On Activity: Participants will build their own anomaly detection model using a provided dataset and start the training process.
Day 3: Implementing Risk Mitigation Strategies
Duration: 1 Hour
Objective: Apply AI models to simulate real-world cybersecurity threat scenarios and learn mitigation techniques.
Session Details:
- Model Evaluation and Tuning: Techniques for evaluating the effectiveness of AI models and tuning them for better performance.
- Simulating Threat Scenarios: Using the trained models to detect and respond to simulated cybersecurity attacks.
- Hands-On Activity: Participants will use their trained models to identify and mitigate threats in a controlled simulation, adjusting their models based on the outcomes.
