Securing AI and Advanced Topics

Instructor: Lanier Watkins

Intermediate Level • 2 weeks to complete at 10 hours a week • Flexible Schedule

What You'll Learn

  • ✓Learn to implement AI-based solutions to detect and prevent credit card fraud in cloud environments.
  • ✓Explore the fundamentals of Generative Adversarial Networks and their applications in generating synthetic data.
  • ✓Gain hands-on experience with black-box and white-box adversarial attacks to assess and enhance model resilience.
  • ✓Master techniques in feature engineering and performance evaluation to optimize AI models for cybersecurity applications.

Skills You'll Gain

Machine Learning
Deep Learning
Threat Detection
Feature Engineering
Generative AI
Reinforcement Learning
Security Testing
Cloud Solutions
Cybersecurity
Artificial Intelligence
Cyber Threat Intelligence
Anomaly Detection
Artificial Intelligence and Machine Learning (AI/ML)
Performance Tuning

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile

Outcomes

  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 6 modules in this course

This course provides a comprehensive exploration of AI-based solutions for credit card fraud detection, emphasizing the implementation and evaluation of advanced algorithms, including Generative Adversarial Networks (GANs). Students will gain practical experience in executing adversarial attacks and optimizing machine learning models, enhancing their ability to develop robust AI systems. Through hands-on projects, participants will synthesize knowledge to address real-world challenges in fraud detection and model resilience.

In this module, we study the background of threats that prevent credit card fraud. Then, we investigate hands-on credit card fraud detection implementations. Also, we discuss metrics to evaluate the performance of credit card fraud detection algorithms.

In this module, we study generative adversarial networks (GANs) background. Then, we investigate a hands-on GAN implementation and how it can be used to develop synthetic data likely indistinguishable from the real data.

In this module, we will discuss black and white-box adversarial attacks. Also, we will explore hands-on implementations of several adversarial attacks.

In this module we will study reinforcement learning (RL) and how it can be used for adversarial attacks. Also, we will study data engineering techniques to optimize datasets to help improve ML model performance.

In this module, we will discuss feature engineering and model optimization techniques. Also, we will explore ML model performance metrics.