Introduction & Setup
Orient the course, establish the lab baseline, and make the first environment decisions before deeper detection and evasion work begins.
View pathResources · Lab Tracks
Practice tracks from the public curriculum.
Lab tracks mirror the learning paths. Candidates can use this page as a practice gallery: pick a section, read the outcomes, and work toward the artifact the section expects.
9 Tracks 8 Assessments 50 Lectures
All Lab Tracks
Each track links back into the canonical curriculum section.
Bot History & Taxonomy
Establish the vocabulary and history behind web automation so later defensive decisions are tied to operator incentives and capabilities.
View pathBrowser Fingerprinting & Device Identity
Move from conceptual bot classes into the browser and network signals that make devices and sessions look coherent or suspicious.
View pathAutomation & Headless Browser Detection
Study how automation stacks leak themselves and how challenge systems force headless browsers to prove they are coherent.
View pathIP Intelligence & Network-Layer Detection
Expand from browser-level signals into reputation, routing, infrastructure attribution, and the operational value of network telemetry.
View pathBehavioral Analytics & ML for Bot Detection
Work from raw interaction traces into behavioral models while keeping explainability and adversarial adaptation in view.
View pathCAPTCHAs & API Security
Connect browser trust and bot pressure to API abuse, credential attacks, token replay, and challenge systems.
View pathCDN, WAF & Production Integration
Take detection logic out of prototypes and place it into CDN, WAF, cache, session, and SOC-adjacent production systems.
View pathFinal Q1 Project & Public Deliverables
End the quarter with a capstone and a publishable artifact set that reflects both engineering rigor and responsible communication.
View path