IP Threat Report
109.105.209.19
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 109.105.209.19 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 109.105.209.0/24
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
⚙️ General Security
Add 109.105.209.19 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
Neighbors in 109.105.209.0/24
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
109.105.209.19 has been assigned a threat score of 80/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
📊 Threat Analysis
Network traffic from 109.105.209.19, located in Los Angeles, United States, operating on the network of Zenlayer Inc, has been classified as malicious by our automated threat scoring engine. During its 38-day observation window, we recorded 975 hostile requests from this IP — roughly 25.7 per day on average. Our records show 109 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. A threat score of 80/100 places this IP in the high-risk category. Blocking at the firewall level is recommended.
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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.
💡 GeoIP Blocking Considerations
Blocking traffic from specific countries reduces attack surface but impacts legitimate international users. Effective geo-based policies use tiered approaches — blocking, rate limiting, or requiring additional verification based on risk assessment.