
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger strong hits: 3 | High-risk paths: shells, RCE vectors, exploits | +75 | |
| Foreign referer | Referer from unrelated external domain | +10 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Block 20.220.219.227 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
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.
20.220.219.227 has been assigned a threat score of 85/100 (Critical). A score this high marks a critical threat actor. This address has demonstrated persistent, aggressive malicious behavior across multiple detection vectors.
20.220.219.227 is registered in Toronto, Canada, operating on the network of Microsoft Corporation. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. The address has been active for 1 days in our monitoring system, producing 60 flagged requests at a rate of ~60/day. Operating from datacenter infrastructure, this IP is typical of addresses used in organized attack operations. Cloud and VPS providers are commonly exploited as launching platforms for automated scanning. With 101 flagged addresses, Canada represents a significant presence in our threat database. A threat score of 85/100 places this IP in the high-risk category. Blocking at the firewall level is recommended.
This IP belongs to a hosting or data center provider. Malicious traffic from hosting infrastructure often originates from compromised VPS instances, rented servers used for scanning campaigns, or abused free-tier cloud accounts. Hosting providers typically respond to abuse reports within 24-72 hours.
Credential stuffing uses stolen username-password pairs from data breaches to attempt logins across many websites. Since users frequently reuse passwords, these automated attacks achieve success rates of 0.1-2%, which translates to thousands of compromised accounts from millions of attempts.
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.