IP Threat Report
158.173.241.63
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 158.173.241.63 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 158.173.241.0/24
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
⚙️ Defensive Recommendations
Block 158.173.241.63 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
Neighbors in 158.173.241.0/24
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
158.173.241.63 has been assigned a threat score of 70/100 (High). The IP is rated as a high-level threat. Network administrators should implement blocking rules and monitor for any connections from this address.
📊 Threat Analysis
Threat intelligence analysis has linked 158.173.241.63 to malicious activity originating from Stockholm, Sweden, operating on the network of Datacamp Limited. The address has been under observation since its initial detection. Over a period of 2 days, this IP generated 154 malicious requests, averaging approximately 77 requests per day. Sweden currently accounts for 151 blocked IPs in our database, making it a significant source of malicious traffic. At 70/100, this IP warrants immediate defensive action.
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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.
💡 Passive DNS for Threat Hunting
Passive DNS databases record historical DNS resolution data, enabling analysts to track domain changes, identify related infrastructure, and discover malicious domains sharing hosting with known threats. This historical context is invaluable for threat investigation.