IP Threat Report
172.213.140.80
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| Directory Scan | Behavioral anomaly detected by automated analysis | +0 | |
| 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 172.213.140.80 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 172.213.140.80 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
172.213.140.80 has been assigned a threat score of 280/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
📊 Threat Analysis
Threat intelligence analysis has linked 172.213.140.80 to malicious activity originating from Milan, Italy, operating on the network of Microsoft Corporation. The address has been under observation since its initial detection. During its 15-day observation window, we recorded 510 hostile requests from this IP — roughly 34 per day on average. Our records show 111 malicious IPs originating from Italy, positioning it as a significant contributor to global threat activity. With a threat score of 280/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
Related Threats
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.