IP Threat Report
131.72.103.50
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| Directory Scan | Behavioral anomaly detected by automated analysis | +0 |
Observed Activity
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Timeline
Recommendations
Actions taken & recommended
- IP 131.72.103.50 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
⚙️ Defensive Recommendations
Block 131.72.103.50 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
Blacklist Status (DNSBL)
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.
Threat Analysis
131.72.103.50 has been assigned a threat score of 60/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.
📊 Threat Analysis
The address 131.72.103.50 originates from Sapucaia do Sul, Brazil, operating on the network of SINALNET Comunicações. It was identified through automated analysis of incoming network traffic across monitored endpoints. The address has been active for 6 days in our monitoring system, producing 765 flagged requests at a rate of ~127.5/day. With 101 flagged addresses, Brazil represents a significant presence in our threat database. The score of 60/100 warrants active monitoring and rate-limiting. Full blocking is advisable for sensitive systems.
Related Threats
Security Intelligence
💡 False Positive Management
False positives erode trust in security systems and waste analyst resources. Effective management requires feedback loops, allowlisting mechanisms, contextual analysis, and regular tuning of detection rules based on operational experience.
💡 Privacy-Preserving Threat Detection
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.