4.0 Learning Outcomes

By the end of this chapter, students will be able to:

• Determine the key concepts of data poisoning attacks in machine learning models.
• Differentiate between poisoning and adversarial attacks.
• Discuss and analyze the real-world implications of data poisoning attacks.
• Describe the three primary attack scenarios in data poisoning.
• Identify different types of poisoning attacks and their impact.
• Analyze real-world examples of poisoning attacks.
• Evaluate the effectiveness of defense mechanisms and mitigation strategies to protect machine learning models.
• Evaluate the trade-offs between security and performance when implementing mitigations.