IP Threat Report
64.62.191.251
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 64.62.191.251 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 64.62.191.0/24
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
⚙️ General Security
Add 64.62.191.251 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
Neighbors in 64.62.191.0/24
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
64.62.191.251 has been assigned a threat score of 85/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
📊 Threat Analysis
64.62.191.251 is registered in Concord, United States, operating on the network of SolidTools Technology, Inc.. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. Our sensors captured 517 malicious requests from this address across a 19-day span, reflecting a sustained attack cadence of ~27.2 requests per day. With 131 flagged addresses, United States represents a significant presence in our threat database. At 85/100, this IP warrants immediate defensive action.
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💡 Dark Web Intelligence Gathering
Monitoring dark web forums and marketplaces provides advance warning of planned attacks, leaked credentials, and compromised data. This intelligence feeds into proactive defense measures before attacks reach their targets.
💡 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.