IP Threat Report
108.181.23.249
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| Malicious Activity | 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 108.181.23.249 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Other malicious IPs detected in the same /24 subnet — consider blocking 108.181.23.0/24
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
⚙️ Defensive Recommendations
Block 108.181.23.249 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
Neighbors in 108.181.23.0/24
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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
108.181.23.249 has been assigned a threat score of 65/100 (High). At this threat level, the IP is considered high risk. Firewall rules should be updated to deny traffic from this source.
📊 Threat Analysis
IP address 108.181.23.249 has been traced to Los Angeles, United States, operating on the network of Psychz Networks. Our threat detection systems have flagged this address based on observed malicious behavior patterns. The address has been active for 23 days in our monitoring system, producing 478 flagged requests at a rate of ~20.8/day. Our records show 102 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. The score of 65/100 warrants active monitoring and rate-limiting. Full blocking is advisable for sensitive systems.
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💡 Insider Threat Indicators
Insider threats — whether malicious or negligent — account for a significant percentage of data breaches. Behavioral analytics detecting unusual access patterns, data downloads, and privilege escalation help identify insider risks before damage occurs.
💡 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.