Ad Fraud Protection

Ad fraud protection helps teams detect suspicious traffic patterns before they distort reporting or waste budget. The goal is to protect media quality, attribution accuracy, and downstream customer analytics.

Fraud patterns to monitor

DriveMetaData-style fraud controls commonly monitor:

• Click fraud: repeated or low-quality clicks that do not represent real intent.
• Bot traffic: automated activity that mimics users.
• Install hijacking: attempts to take credit for installs influenced by another source.
• Click flooding: unusually high click volume before conversions.
• SDK spoofing: fabricated app events sent without genuine user behavior.
• In-app event fraud: suspicious post-install actions that inflate performance.
• Ad stack tampering: manipulated partner, device, or campaign signals.

Signals used for review

Fraud models and rules can consider patterns such as:

• Click-to-install time distribution.
• Device, IP, geo, and user-agent consistency.
• Event frequency and sequence.
• Partner-level anomaly changes.
• Campaign and creative outliers.
• Repeated identifiers or suspiciously clean identifiers.
• Conversion quality after install or signup.

note

Fraud detection should support review and action. It should not be the only factor used to make high-impact decisions about partners, users, or revenue.

Rule-based and AI-assisted detection

Rule-based detection is useful for known patterns, such as blocking traffic from disallowed geographies or flagging impossible event sequences.

AI-assisted detection is useful when suspicious behavior appears as a pattern across many signals, such as abnormal conversion timing, sudden partner changes, or events that look valid in isolation but unusual in context.

Review workflow

1. Monitor alerts and anomaly reports.
2. Filter by partner, campaign, geo, device, and event type.
3. Compare suspicious traffic against normal cohorts.
4. Review conversion quality after the suspected event.
5. Decide whether to label, exclude, block, or escalate.
6. Document the action and monitor recurrence.

Actions teams may take

Depending on internal policy and available integrations, teams may:

• Exclude suspicious activity from reports.
• Add traffic sources to a review list.
• Block sources through partner or campaign settings.
• Adjust attribution credit.
• Pause campaigns pending partner review.
• Create alerts for repeated patterns.

Troubleshooting

Issue	What to review
High installs with low retention	Click-to-install timing, partner mix, device patterns, post-install events
Sudden ROAS drop	Cost import, conversion mapping, partner anomalies, fraud exclusions
Too many false positives	Rule thresholds, seasonality, campaign launch context, geo expectations
Fraud appears after activation	Audience source quality, channel eligibility, suppression rules

Good operating practices

• Review fraud results with marketing and analytics together.
• Keep a documented reason for major exclusions.
• Separate suspected fraud from confirmed policy violations.
• Monitor partner quality over time, not only during spikes.
• Recheck historical reports when fraud rules change materially.