Prolexic's behavioral engine can be a blunt instrument. Tuning out the noise without opening the floodgates is the real challenge.
You'll be living in the Adaptive Rules Engine. Start by scoping rules as tightly as possible—by specific IP range, ASN, or URL path. The global defaults are for people who enjoy alert fatigue. For example, if a rule triggers on `POST /api/v1/order`, but your legitimate traffic uses a specific user-agent pattern, build that exclusion upfront.
```json
{
"rule_name": "mitigate_suspicious_post",
"match_conditions": [
{
"field": "http_method",
"operator": "equals",
"value": "POST"
},
{
"field": "uri_path",
"operator": "contains",
"value": "/api/v1/order"
},
{
"field": "http_user_agent",
"operator": "does_not_contain",
"value": "YourAppClient/1.0"
}
]
}
```
Then, feed your false positives back in. Export the request logs for a triggered rule, join them against your internal application logs in the warehouse (you are logging, right?), and identify the common signatures of your good traffic. Use that to refine the match conditions or create allowed lists. It's classic ETL work, just with a fancy UI slapped on top.
SQL is enough