Looking for technical insights into how App Store install farms actually work under the hood

Hi everyone,

I'm currently researching on mobile app fraud detection and I'm trying to better understand how iOS App Store install farms actually operate from a technical perspective.

I'm not interested in buying installs. I'm trying to understand the underlying architecture so I can model a realistic research.

Some questions I'm curious about:

• Are these farms mostly physical iPhones, cloud devices, or something else?
• How is the install process coordinated across hundreds or thousands of devices?
• Do operators simply automate Apple IDs downloading apps, or is there more involved?
• How do they rotate Apple IDs, IP addresses, regions, and devices?
• Do installs usually come from search, browse, direct links, or a mix?
• What does the backend infrastructure generally look like (device management, task distribution, dashboards, etc.)?
• Are there any public write-ups, talks, or reverse engineering articles that accurately describe how modern App Store install farms operate?

I'm mainly interested in understanding what happens under the hood rather than the business side. Even high-level explanations or links to technical resources would be appreciated.

Thanks!

Most of the “modern” stuff you’ll find in public is either old Android farm content or vendor blog fluff, so I wouldn’t expect a real current iOS blueprint floating around.

For research I’d model it more from signals than mechanics: unnatural install timing, same app/session path repeated too cleanly, Apple ID/device age patterns, geo/IP mismatch, low post-install entropy, weird retention curves, etc. iOS farms are usually not magic cloud emulators like people imagine, Apple makes that painful... more like controlled device pools + task queue + some messy account/network management around it.

Look at Adjust/AppsFlyer/Singular fraud reports, academic papers around mobile ad fraud, and Apple docs on DeviceCheck/App Attest/SKAdNetwork limitations. Not perfect, but enough to build a realistic threat model without getting into the “how do I run one” side.