AI for Cybersecurity: Threat Detection and Risk Mitigation
Enhancing Cybersecurity with AI: Detect and Mitigate Threats with Precision
About This Course
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.
Aim
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.
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.
Workshop Structure
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.
Who Should Enrol?
Cybersecurity professionals, AI researchers, data scientists, IT professionals, and academic researchers.
Important Dates
Registration Ends
10/27/2024
IST 1:00 pm
Workshop Dates
10/27/2024 – 10/29/2024
IST 5 PM
Workshop Outcomes
- Develop AI models for real-time threat detection and anomaly detection.
- Implement predictive risk modeling to identify potential cyber threats.
- Build a comprehensive AI-based cybersecurity system for real-time monitoring.
- Apply AI tools to mitigate and prevent cyberattacks.
- Gain hands-on experience with AI cybersecurity solutions.
Fee Structure
Student Standard fee
₹1499 | $40
Ph.D. Scholar / Researcher Standard fee
₹1999 | $45
Academician / Faculty Standard fee
₹2999 | $50
Industry Professional Standard fee
₹4999 | $75
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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