IP Threat Report
157.230.129.81
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 157.230.129.81 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
⚙️ Defensive Recommendations
Block 157.230.129.81 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
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
157.230.129.81 has been assigned a threat score of 100/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
📊 Threat Analysis
The address 157.230.129.81 originates from Santa Clara, United States, operating on the network of DigitalOcean, LLC. It was identified through automated analysis of incoming network traffic across monitored endpoints. Over a period of 4 days, this IP generated 125 malicious requests, averaging approximately 31.3 requests per day. United States currently accounts for 101 blocked IPs in our database, making it a significant source of malicious traffic. With a threat score of 100/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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💡 Digital Forensics Fundamentals
Digital forensics preserves and analyzes electronic evidence following attacks. Proper chain of custody, forensic imaging, timeline reconstruction, and artifact analysis are essential for understanding attack scope, attribution, and preventing recurrence.
💡 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.