Handle Fraudulent Traffic
You have identified a fraud source and want to automatically handle that traffic. Adsafee does not block visits directly — it classifies them, and you decide what to do: flag, redirect to traffic back, or show a challenge.
Example: Proxy-like Traffic from a Specific Country
You have determined that suspicious traffic is coming from China through proxy-like infrastructure.
1. Create a Rule
Go to Rules > Add Rule.
2. Set Up Conditions
Create a condition group with AND logic:
| Field | Operator | Value |
|---|---|---|
geo_country | equals | CN |
traffic_signal_name | equals | proxy_like |
This means: the rule will trigger when a visit is both from China AND has proxy-like context.
3. Choose an Action
| Action | What happens |
|---|---|
| Flag as GIVT / SIVT | The visit will be flagged — you will see this in statistics, but the visitor stays on the site |
| Send to Traffic Back | The visitor will be redirected to the URL specified in the integration settings |
| Show Challenge | The visitor will see a verification check. If they pass — they stay; if they fail — they won't reach the site |
4. Attach to an Integration
A rule does not work on its own. Go to the settings of the relevant integration, add the rule to the list, and save.
Other Common Scenarios
Flag crawlers
Condition: is_crawler_ua equals trueAction: Flag as GIVT Crawlers will appear in statistics as GIVT.
Send webview traffic to traffic back
Condition: is_webview equals trueAction: Send to Traffic Back Visitors from in-app embedded browsers will be redirected.
Show a challenge to proxy-like traffic
Condition: traffic_signal_name equals proxy_likeAction: Show Challenge Real users behind a VPN will pass the check; bots will not.
Flag a trusted source as clean
Condition: sub1 equals trusted_partner_idAction: Flag as Good
Caution
The "Flag as Good" rule overrides automatic classification. Use it carefully and only for sources you are confident about.
What's Next
- Set Up a Challenge — interactive verification of suspicious visits
- Find Fraud Sources — how to discover where fraud is coming from
- Filtering Rules — more on rule logic