IP Threat Report
177.66.3.157
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 177.66.3.157 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 177.66.3.157 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
177.66.3.157 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 177.66.3.157, geolocated to Nova Iguaçu, Brazil, operating on the network of S.C. RIO TELECOMUNICACOES E INFORMATICA LTDA, as a source of suspicious network activity. The address has been active for 6 days in our monitoring system, producing 765 flagged requests at a rate of ~127.5/day. Brazil currently accounts for 101 blocked IPs in our database, making it a significant source of malicious traffic. The score of 60/100 warrants active monitoring and rate-limiting. Full blocking is advisable for sensitive systems.
Related Threats
Security Intelligence
💡 SQL Injection Campaigns
SQL injection remains one of the most common web attack vectors. Attackers inject malicious SQL code through input fields to extract database contents, modify data, or gain administrative access. Automated scanners test for SQLi vulnerabilities at massive scale.
💡 Behavioral Analysis vs Signature Detection
Signature-based detection matches known attack patterns but misses novel threats. Behavioral analysis identifies anomalies in request patterns, timing, and volume, catching zero-day attacks that signatures cannot recognize.