IP Threat Report
54.223.70.23
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 54.223.70.23 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 54.223.70.23 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
54.223.70.23 has been assigned a threat score of 80/100 (Critical). This places it in the critical threat category. Immediate blocking is strongly advised across all network perimeters.
📊 Threat Analysis
Our monitoring infrastructure has identified 54.223.70.23, geolocated to Beijing, China, operating on the network of Beijing Guanghuan Xinwang Digital, as a source of suspicious network activity. The address has been active for 4 days in our monitoring system, producing 531 flagged requests at a rate of ~132.8/day. Our records show 101 malicious IPs originating from China, positioning it as a significant contributor to global threat activity. 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.