IP Threat Report
72.56.191.148
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 72.56.191.148 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 72.56.191.0/24
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
⚙️ General Security
Add 72.56.191.148 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
Neighbors in 72.56.191.0/24
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
72.56.191.148 has been assigned a threat score of 75/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.
📊 Threat Analysis
The address 72.56.191.148 originates from Moscow, Russia, operating on the network of WS Telecom Inc. It was identified through automated analysis of incoming network traffic across monitored endpoints. Over a period of 13 days, this IP generated 952 malicious requests, averaging approximately 73.2 requests per day. Russia currently accounts for 130 blocked IPs in our database, making it a significant source of malicious traffic. A threat score of 75/100 places this IP in the high-risk category. Blocking at the firewall level is recommended.
Related Threats
Security Intelligence
💡 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.
💡 Residential Proxy Networks
Residential proxies route traffic through real home internet connections, making malicious traffic appear to come from legitimate users. Some networks install proxy software bundled with free applications, unknowingly conscripting millions of devices.