AI for Cybersecurity: Threat Detection and Risk Mitigation

This Mentor Based workshop delves into how AI revolutionizes cybersecurity, focusing on AI-based threat detection, predictive risk models, and mitigation strategies. Participants will learn about anomaly detection and predictive techniques to develop AI models for detecting and preventing malicious activities.

To equip PhD scholars and academicians with advanced skills in AI-driven cybersecurity, focusing on threat detection and risk mitigation. This course covers anomaly detection, predictive modeling, and building AI systems to identify and mitigate cyber threats.

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.

Cybersecurity professionals, AI researchers, data scientists, IT professionals, and academic researchers.