IP Threat Report
67.205.185.204
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 67.205.185.204 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Other malicious IPs detected in the same /24 subnet — consider blocking 67.205.185.0/24
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
⚙️ General Security
Add 67.205.185.204 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
Neighbors in 67.205.185.0/24
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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
67.205.185.204 has been assigned a threat score of 120/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
📊 Threat Analysis
Network traffic from 67.205.185.204, located in North Bergen, United States, operating on the network of DigitalOcean, LLC, has been classified as malicious by our automated threat scoring engine. During its 4-day observation window, we recorded 373 hostile requests from this IP — roughly 93.3 per day on average. United States currently accounts for 102 blocked IPs in our database, making it a significant source of malicious traffic. A score of 120/100 places this address in the top tier of severity. Block and investigate any historical connections.
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💡 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.
💡 Submarine Cable and Internet Exchange Points
Internet traffic routing through a limited number of submarine cables and exchange points creates natural chokepoints. Understanding these routing patterns helps explain geographic clustering of certain attack types and latency-based scanning behaviors.