IP Threat Report
181.66.137.54
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 181.66.137.54 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 181.66.137.54 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
181.66.137.54 has been assigned a threat score of 60/100 (High). The IP is rated as a high-level threat. Network administrators should implement blocking rules and monitor for any connections from this address.
📊 Threat Analysis
Our monitoring infrastructure has identified 181.66.137.54, geolocated to Lima, PE, operating on the network of Telefonica del Peru S.A.A., as a source of suspicious network activity. The address has been active for 13 days in our monitoring system, producing 99 flagged requests at a rate of ~7.6/day. PE currently accounts for 37 blocked IPs in our database, making it a notable source of malicious traffic. At 60/100, this IP presents a meaningful threat. Implement rate limiting with escalation to blocking.
Related Threats
Security Intelligence
💡 Machine Learning in Threat Detection
Machine learning models analyze vast amounts of network traffic to identify attack patterns invisible to rule-based systems. Supervised models classify known attack types while unsupervised models detect anomalies that may indicate novel threats.
💡 TLS Certificate Misconfigurations
Expired, self-signed, or misconfigured TLS certificates create security vulnerabilities and trust issues. Certificate monitoring, automated renewal through ACME protocols, and proper certificate chain configuration prevent both security gaps and service disruptions.