Overview What You Will Learn Curriculum Instructor

Course Overview

Adversarial attacks pose a serious threat to your machine learning systems, putting your data, models, and reputation at risk. The rise in adversarial Machine Learning (AML) attacks demands immediate action to protect your AI solutions. 

This course provides a comprehensive, hands-on guide to understanding and defending against adversarial Machine Learning attacks. You’ll start with a solid foundation in Machine Learning, explore attack methods in depth, and learn how to implement advanced defenses using industry-standard tools like TensorFlow, PyTorch, and CleverHans. In the final section, the course will provide insights into future directions and emerging trends in adversarial machine learning. You’ll also receive a checklist to help protect against adversarial machine learning threats, ensuring you leave with practical strategies to apply in real-world environments. The course will end with a short guide for the learners on how they can further leverage what they learned in this course by pursuing EC-Council’s Mastering ChatGPT for Ethical Hacking course.

By the end of this course, you’ll have a detailed understanding of adversarial Machine Learning attacks, you’ll also be able to implement defensive measures against the threat, and you’ll be fully equipped to protect your machine learning models from current and future threats. Take control now and secure your AI with expert knowledge and practical solutions.

What You Will Learn

  • Gain a detailed understanding of adversarial machine learning attacks and defense strategies.
  • Learn how to identify, classify, and mitigate common attack types like evasion attacks, data poisoning, and model extraction.
  • Implement advanced defenses like defensive distillation, gradient masking, and Byzantine-resilience algorithms.
  • Use libraries such as TensorFlow, PyTorch, and CleverHans to defend against adversarial attacks with real-world examples.
  • Understand white-box and black-box attacks, and how to protect against both types of threats.
  • Explore emerging trends in adversarial machine learning and meet regulatory and ethical standards in AI security.

Program Curriculum

  • Foundations of Machine Learning
  • Why Cybersecurity Professionals Must Understand AI
  • Introduction to Machine Learning
  • Understanding Common ML Algorithms and Their Applications
  • The Machine Learning Process
  • Chapter 1 Quiz

  • What Is Adversarial Machine Learning?
  • What is Adversarial Machine Learning, and Why Now?
  • Gradient-Based Learning: The Vulnerability Inside
  • Threat Models: White-box, Black-box, Gray-box
  • The AI Battle Royal: Generative Adversarial Networks
  • Ethics, Regulation, and Trust Implications of AML
  • Chapter 2 Quiz

  • How and Where Adversaries Attack Machine Learning
  • Why is the Machine Learning Process Vulnerable?
  • Uncle Ronnie is Poisoning Your Data: Training Stage Attacks
  • How Adversaries Evade or Corrupt ML Models: Inference Stage Attacks
  • Is Your Training Data Private?
  • An AML Taxonomy
  • Chapter 3 Quiz

  • Training Stage Attack Techniques
  • How Adversaries Can Disrupt Training: Feature Manipulation and Label Flipping
  • Lab – Feature Manipulation Poisoning
  • Lab – Label Flipping for Model Subversion
  • Chapter 4 Quiz

  • Inference Stage Attacks
  • Adversarial Methods – FGSM, PGD, Carlini, & Wagner
  • Lab – Fast Gradient Sign Method (FGSM) Attack on MNIST
  • Lab – Projected Gradient Descent (PGD) Attack on MNIST
  • Attacking Text-Based Classifiers
  • Transferability of Attacks
  • Real-World Use Case – An Email Example
  • Privacy Concerns - How Adversaries Extract Training Data or Infer Membership
  • Chapter 5 Quiz

  • Defending Against AML
  • Do Not Forget the Cybersecurity Fundamentals
  • ML-Specific Security Measures: An Overview
  • Keeping Private Information Private: Privacy-Preserving ML
  • Gradient Masking and Defensive Distillation
  • Byzantine-resilient Algorithms
  • Adversarial Training and Testing
  • Rate Limiting
  • Lab – Adversarial Training to Increase Model Robustness
  • Lab – Verifying Robustness
  • Chapter 6 Quiz

  • Future Trends and Governance
  • AML in the Age of GenAI, LLMs, and Autonomous Agents
  • Explainability and Robustness are Pillars of Trust
  • Incorporating AML Into Governance and Risk
  • Frameworks for Responsible AI
  • Chapter 7 Quiz
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Instructor

Donnie W. Wendt

Dr. Donnie Wendt, author of The Cybersecurity Trinity and AI Strategy and Security, is a leading authority in AI security with over 30 years of experience in software development, cybersecurity, and AI operationalization. As a retired Principal Security Researcher at Mastercard, he advanced AI-driven defense strategies and explored emerging cyber threats. Now an advisor to Whiteglove AI and Styrk.ai, he promotes responsible AI innovation. A dedicated educator at Columbus State University, Donnie empowers future cyber defenders through hands-on learning. He holds a Doctorate in Computer Science (Information Security) and continues to shape the secure future of AI and cybersecurity.

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