IP Threat Report
143.198.235.204
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 143.198.235.204 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 143.198.235.204 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
143.198.235.204 has been assigned a threat score of 120/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
📊 Threat Analysis
IP address 143.198.235.204 has been traced to Santa Clara, United States, operating on the network of DigitalOcean, LLC. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 198 malicious requests from this address across a 4-day span, reflecting a sustained attack cadence of ~49.5 requests per day. Our records show 101 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. A score of 120/100 places this address in the top tier of severity. Block and investigate any historical connections.
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💡 Security Header Best Practices
HTTP security headers provide defense-in-depth with minimal implementation effort. Key headers include Strict-Transport-Security, X-Content-Type-Options, X-Frame-Options, Referrer-Policy, and Permissions-Policy, each addressing specific attack vectors.
💡 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.