Defending Against Botnets and Fraud

This unit synthesizes case studies and practical techniques to design, deploy, and operationalize defenses against botnets, CAPTCHA farms, and automated fraud. Students will also examine privacy vs. security trade-offs and evaluate privacy-preserving approaches such as private access tokens to inform secure, compliant defenses.

Learning Objectives

• Analyze botnet case studies to identify common tactics, techniques, and procedures (TTPs) and draw lessons for defensive design
• Apply a layered defense strategy by selecting and mapping appropriate technology stack components (e.g., detection, fingerprinting, rate limiting, private access tokens) to mitigate bot and fraud threats
• Demonstrate the ability to develop operational response plans, including incident playbooks and controls to defend against CAPTCHA farms and orchestrated abuse
• Evaluate privacy versus security trade-offs and recommend privacy-preserving mitigation options that balance risk reduction, user experience, and regulatory compliance