
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Directory Scan | Behavioral anomaly detected by automated analysis | +0 | |
| DDoS / Flood | Behavioral anomaly detected by automated analysis | +0 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Add 20.9.87.130 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
This IP was checked against major DNS-based blacklists used by mail servers and firewalls worldwide.
Checked: Spamhaus, SpamCop, Barracuda, SORBS, CBL, UCEProtect. Results may change over time.
20.9.87.130 has been assigned a threat score of 280/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
The address 20.9.87.130 originates from Des Moines, United States, operating on the network of Microsoft Corporation. It was identified through automated analysis of incoming network traffic across monitored endpoints. The address has been active for 25 days in our monitoring system, producing 168 flagged requests at a rate of ~6.7/day. United States currently accounts for 103 blocked IPs in our database, making it a significant source of malicious traffic. A score of 280/100 places this address in the top tier of severity. Block and investigate any historical connections.
Distributed denial of service attacks overwhelm infrastructure with traffic volume. Effective mitigation combines always-on traffic scrubbing, anycast network distribution, rate limiting, and the ability to quickly scale absorption capacity during attacks.
CAPTCHAs remain a primary bot defense but face increasing bypass rates from AI-powered solvers. Modern alternatives include invisible behavioral analysis, proof-of-work challenges, and device fingerprinting that detect bots without impacting user experience.