IP Threat Report
167.71.246.78
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 167.71.246.78 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
⚙️ General Security
Add 167.71.246.78 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
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
167.71.246.78 has been assigned a threat score of 95/100 (Critical). A score this high marks a critical threat actor. This address has demonstrated persistent, aggressive malicious behavior across multiple detection vectors.
📊 Threat Analysis
Network traffic from 167.71.246.78, located in Clifton, United States, operating on the network of DigitalOcean, LLC, has been classified as malicious by our automated threat scoring engine. The address has been active for 12 days in our monitoring system, producing 521 flagged requests at a rate of ~43.4/day. Our records show 101 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. At 95/100, 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.