IP Threat Report
217.116.58.71
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| Malicious Activity | 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 217.116.58.71 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 217.116.58.0/24
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
⚙️ Defensive Recommendations
Block 217.116.58.71 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
Neighbors in 217.116.58.0/24
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
217.116.58.71 has been assigned a threat score of 68/100 (High). The IP is rated as a high-level threat. Network administrators should implement blocking rules and monitor for any connections from this address.
📊 Threat Analysis
IP address 217.116.58.71 has been traced to Tyumen, Russia, operating on the network of Russian company LLC. Our threat detection systems have flagged this address based on observed malicious behavior patterns. During its 27-day observation window, we recorded 587 hostile requests from this IP — roughly 21.7 per day on average. Russia currently accounts for 126 blocked IPs in our database, making it a significant source of malicious traffic. At 68/100, this IP presents a meaningful threat. Implement rate limiting with escalation to blocking.
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.
💡 Mobile Malware Distribution
Mobile malware reaches devices through unofficial app stores, malicious links, and even occasionally through official stores using obfuscation techniques. Banking trojans, spyware, and ransomware variants specifically designed for mobile platforms continue to proliferate.