IP Threat Report
95.179.16.123
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| 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 95.179.16.123 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 95.179.16.123 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
95.179.16.123 has been assigned a threat score of 80/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
📊 Threat Analysis
Threat intelligence analysis has linked 95.179.16.123 to malicious activity originating from Yelets, Russia, operating on the network of Rostelecom networks. The address has been under observation since its initial detection. During its 5-day observation window, we recorded 839 hostile requests from this IP — roughly 167.8 per day on average. With 101 flagged addresses, Russia represents a significant presence in our threat database. A threat score of 80/100 places this IP in the high-risk category. Blocking at the firewall level is 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.