<b>Device farms vs emulator fraud: your detection needs both lenses</b>

<b>Device farms vs emulator fraud: your detection needs both lenses</b>
In-app install fraud splits into real-device farms and emulated traffic. They leave different fingerprints, and a single tool rarely nails both.

<b>Emulator/bot traffic</b>
✓ Caught by device-signal checks (sensor data, build props, GPU strings)
✗ Sophisticated bots now spoof sensor data convincingly

<b>Real device farms</b>
✓ Harder to spoof behavior — caught via engagement-pattern anomalies post-install
✗ Look like real devices; only behavioral cohorts expose them

The layered approach that works: device-fingerprint rejection at the door (mFilterIt, Interceptd) plus post-install behavioral cohorting (your own retention curves by source). Farms show abnormally flat or cliff-edge retention.

The mistake: relying on the MMP's device-signal flag alone and ignoring the retention shape that exposes farms.

<b>Verdict:</b> combine signal-based pre-bid rejection with behavioral post-install audit; neither alone suffices.
<b>Best for:</b> UA teams buying from non-premium incentivized sources.