IP Threat Report
34.40.75.21
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| DDoS / Flood | 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 34.40.75.21 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 34.40.75.21 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
34.40.75.21 has been assigned a threat score of 195/100 (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 34.40.75.21, located in Frankfurt, Germany, operating on the network of Google LLC, has been classified as malicious by our automated threat scoring engine. The address has been active for 7 days in our monitoring system, producing 158 flagged requests at a rate of ~22.6/day. Our records show 101 malicious IPs originating from Germany, positioning it as a significant contributor to global threat activity. At 195/100, this is an extremely high-risk address. All traffic should be considered hostile.
Related Threats
🇩🇪 Top threats from Germany
15.220.152.241 (320)35.246.204.221 (320)185.168.29.98 (313)77.81.65.244 (313)64.89.163.68 (308)View all →Security Intelligence
💡 DDoS Mitigation Approaches
Distributed denial of service attacks overwhelm infrastructure with traffic volume. Effective mitigation combines always-on traffic scrubbing, anycast network distribution, rate limiting, and the ability to quickly scale absorption capacity during attacks.
💡 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.