IP Threat Report
18.237.196.146
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 18.237.196.146 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 18.237.196.146 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
18.237.196.146 has been assigned a threat score of 238/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
📊 Threat Analysis
IP address 18.237.196.146 has been traced to Portland, United States, operating on the network of Amazon.com, Inc.. Our threat detection systems have flagged this address based on observed malicious behavior patterns. During its 1-day observation window, we recorded 13 hostile requests from this IP — roughly 13 per day on average. Our records show 101 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. A score of 238/100 places this address in the top tier of severity. Block and investigate any historical connections.
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💡 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.