How To Detect Mobile Ad Fraud In Your MMP Data

• Click spamming (click flooding): huge volumes of fake clicks so that some organic installs are randomly attributed to the fraudster.
• Click injection: malware on the device detects an app install and fires a click just before the first open, stealing attribution.
• SDK spoofing: fake install and event signals sent directly to the MMP without a real device.
• Bots and emulators: scripted or virtual devices that generate installs and sometimes fake events.
• Device farms: rooms of real devices operated manually or semi-automatically to install apps and complete simple events.
• Incentive abuse: real users motivated only by a reward, who never engage after claiming it.

CTIT — the time between the attributed click and the first app open — is the single most revealing fraud signal. Export install-level data with click and install timestamps and plot the distribution by source.

A healthy source shows a distribution with most installs within minutes to a few hours, tailing off. Click injection shows a spike of installs with CTIT of a few seconds — faster than a human can download and open most apps. Click spamming shows a long, flat distribution stretching across the full attribution window, because the 'clicks' had no real connection to the install.

Calculate conversion rate (installs ÷ clicks) by source and placement. Extremely low rates paired with enormous click volume suggest click spamming. Extremely high CTR combined with poor retention suggests accidental or forced clicks. Look for sources whose click volume grows without any change in spend or bid.

Fraudulent installs rarely behave like real users. For each source, compare against your organic and best-performing paid sources:

• D1 and D7 retention — near-zero retention is a red flag.
• Sessions per user and session length — bots produce identical, short sessions.
• Event sequences — identical event timing across many users suggests scripts.
• Revenue — farms can produce registrations, but rarely genuine purchases.

Break installs down by device model, OS version, language, carrier and IP. Fraud often concentrates: an unusual share of a single old device model, OS versions that do not match the claimed market, device language inconsistent with geo, data-center IP ranges, or many installs from the same IP subnet. New-device rate — the share of installs from devices never seen before — that is unusually high can indicate device-ID resetting in farms.

Compare click geo, install geo and event geo. Mismatches are suspicious. Plot installs by hour in local time — real users follow daily rhythms, while some bots and farms operate flat around the clock or in the fraudster's time zone rather than the target market's.

Your MMP's fraud suite reports rejected installs and the reason (for example, CTIT anomaly, blocked IP, invalid signature). Review rejection rates by source weekly. A source with a high rejection rate is likely sending more fraud that slips through. Make sure rejected installs are excluded from billing and that your DSP or networks receive the rejection postbacks.

Attribution fraud — click spamming and injection in particular — steals credit for organic installs. If pausing a source produces no drop in total installs, the source was likely claiming installs it did not cause. Geo holdouts and on/off tests are blunt but effective ways to confirm.

• Block confirmed fraudulent sources and placements at sub-publisher level.
• Tighten MMP rules: CTIT thresholds, IP blocklists, signature validation for SDK spoofing.
• Optimize on deep events that are harder to fake, not just installs.
• Agree clear terms with partners on how rejected or fraudulent conversions are treated.
• Document patterns so the team recognizes them quickly next time.

Not every anomaly is fraud. A short CTIT spike can come from a lightweight app that installs quickly on fast networks; a retention dip can follow a product release; a device concentration can reflect a popular local handset. Before blocking, compare the suspicious source with a trusted source in the same geo and OS over the same period. If the pattern appears everywhere, the cause is probably in your app or market, not in the traffic. Fraud is a pattern that is specific to a source — that specificity is what makes it actionable.

Ad hoc analysis catches fraud once; a dashboard catches it every week. You do not need a complex tool — a sheet or BI view built on your MMP's raw data export is enough. The goal is to see each source's fingerprint at a glance and to spot changes quickly.

• CTIT distribution per source, with the share of installs under a short threshold highlighted.
• Clicks, installs and click-to-install conversion rate per source and sub-publisher.
• D1 and D7 retention and event rates per source, against your organic baseline.
• Rejected-install rate and top rejection reasons per source.
• Top device models, OS versions and IP subnets per source, with week-over-week change.
• Installs by local hour of day per source.

Fraud patterns differ by platform. On Android, the Google Play install referrer provides timestamps for the click and the install start, which helps MMPs detect click injection: if the attributed click happened after the download began, it could not have caused the install. Android's openness also makes it the main target for malware-based attribution fraud.

On iOS, device-level attribution is limited to consented users, and SKAN postbacks are signed by Apple, which makes some forms of spoofing harder. However, aggregated data also hides patterns that would be visible at device level. iOS fraud review therefore leans more on aggregate anomalies — unusual conversion-value distributions, sudden volume spikes from specific source identifiers — and on incrementality testing.

When you find a suspicious source, share the evidence: the CTIT chart, the retention comparison, the device breakdown. Good partners will investigate, block the sub-publisher and agree how affected conversions are treated. Keep the conversation factual and specific; it is far easier to resolve a dispute when both sides look at the same install-level data and the same definitions.

Agree the rules in advance where possible — which MMP rejection reasons are excluded from billing, how post-attribution fraud discovered later is handled, and how quickly sources are blocked once flagged. Clear terms prevent most disputes before they start.

Adora X filters invalid traffic before the bid, monitors CTIT, engagement and placement anomalies after delivery, and consumes your MMP's rejection postbacks so flagged sources are removed from future buying. Source-level transparency means you can run every check in this guide against our traffic.