Defending and Responding to Botnets

This capstone unit synthesizes prior learning to design, implement, and operationalize defenses against botnets and large-scale fraud. Students will analyze case studies, build layered technology stacks and response playbooks, and weigh privacy versus security trade-offs including privacy-preserving approaches such as Private Access Tokens.

Learning Objectives

• Analyze a botnet case study to identify indicators of compromise, attack vectors, affected assets, and produce a prioritized list of mitigation and monitoring actions.
• Apply a layered defense strategy by selecting and justifying components of a technology stack (e.g., WAF, rate limiting, device fingerprinting, telemetry, threat intelligence) to mitigate specific bot and fraud scenarios.
• Demonstrate incident response and operationalization by creating a runbook that includes detection triggers, containment steps, remediation procedures, CAPTCHA-farm countermeasures, and metrics for continuous monitoring.
• Evaluate privacy versus security trade-offs and recommend privacy-preserving detection approaches (such as Private Access Tokens, minimized telemetry, and consent models) that balance regulatory requirements and detection efficacy.