Workshop Objectives:

• Learn AI techniques for advanced threat detection.
• Implement predictive modeling for cybersecurity risk management.
• Develop AI-based risk mitigation strategies.
• Build AI systems for real-time threat detection.
• Gain hands-on experience with AI-driven cybersecurity tools.

What you will learn?

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.