IP Threat Report
31.59.27.66
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 31.59.27.66 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
⚙️ Defensive Recommendations
Block 31.59.27.66 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
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
31.59.27.66 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 31.59.27.66 originates from London, United Kingdom, operating on the network of Leaseweb UK Limited. It was identified through automated analysis of incoming network traffic across monitored endpoints. Our sensors captured 197 malicious requests from this address across a 30-day span, reflecting a sustained attack cadence of ~6.6 requests per day. Our records show 101 malicious IPs originating from United Kingdom, positioning it as a significant contributor to global threat activity. At 75/100, this IP warrants immediate defensive action.
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💡 Data Breach Impact Assessment
The impact of data breaches extends beyond immediate financial losses. Regulatory fines, legal liability, reputational damage, and customer churn create long-term costs that often exceed the direct costs of incident response and remediation.
💡 Autonomous System Analysis
Analyzing attack patterns at the AS (Autonomous System) level reveals which networks harbor the most malicious activity. Some ASes have abuse rates orders of magnitude higher than average, indicating lax enforcement of acceptable use policies.