
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.
Block 45.128.199.121 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
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.
45.128.199.121 has been assigned a threat score of 270/100 (Critical). A score this high marks a critical threat actor. This address has demonstrated persistent, aggressive malicious behavior across multiple detection vectors.
Our monitoring infrastructure has identified 45.128.199.121, geolocated to Amsterdam, Netherlands, operating on the network of Clouvider Limited, as a source of suspicious network activity. Our sensors captured 502 malicious requests from this address across a 10-day span, reflecting a sustained attack cadence of ~50.2 requests per day. Our records show 108 malicious IPs originating from Netherlands, positioning it as a significant contributor to global threat activity. A score of 270/100 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.
Advanced techniques enable threat detection while minimizing privacy impact. Encrypted DNS, differential privacy in analytics, and federated learning for threat models allow effective security monitoring without unnecessary surveillance of legitimate user behavior.