IP Threat Report
146.196.64.107
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 146.196.64.107 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 146.196.64.107 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
146.196.64.107 has been assigned a threat score of 95/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
📊 Threat Analysis
146.196.64.107 is registered in Ho Chi Minh City, Vietnam, operating on the network of VinaHost Co. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. The address has been active for 21 days in our monitoring system, producing 15 flagged requests at a rate of ~0.7/day. Our records show 101 malicious IPs originating from Vietnam, positioning it as a significant contributor to global threat activity. With a threat score of 95/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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💡 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.
💡 IoT Device Compromise Patterns
Internet of Things devices are prime targets for botnet recruitment due to weak default credentials, infrequent updates, and always-on connectivity. Compromised IoT devices generate persistent scanning and attack traffic without their owners knowledge.