IP Threat Report
158.173.241.35
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 158.173.241.35 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 158.173.241.35 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
158.173.241.35 has been assigned a threat score of 90/100 (Critical). This places it in the critical threat category. Immediate blocking is strongly advised across all network perimeters.
📊 Threat Analysis
IP address 158.173.241.35 has been traced to Stockholm, Sweden, operating on the network of Datacamp Limited. Our threat detection systems have flagged this address based on observed malicious behavior patterns. The address has been active for 21 days in our monitoring system, producing 1,212 flagged requests at a rate of ~57.7/day. With 151 flagged addresses, Sweden represents a significant presence in our threat database. With a threat score of 90/100, 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.
💡 DNS Sinkholing for Malware Defense
DNS sinkholing redirects queries for known malicious domains to controlled IP addresses. This technique blocks malware communication, prevents data exfiltration, and identifies compromised internal hosts attempting to contact command-and-control servers.