IP Threat Report
79.137.205.130
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 79.137.205.130 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 79.137.205.130 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
79.137.205.130 has been assigned a threat score of 105/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
📊 Threat Analysis
The address 79.137.205.130 originates from Amsterdam, Netherlands, operating on the network of Aeza International LTD. It was identified through automated analysis of incoming network traffic across monitored endpoints. Over a period of 5 days, this IP generated 810 malicious requests, averaging approximately 162 requests per day. Netherlands currently accounts for 101 blocked IPs in our database, making it a significant source of malicious traffic. At 105/100, this is an extremely high-risk address. All traffic should be considered hostile.
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💡 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.
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