IP Threat Report
162.158.111.124
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 162.158.111.124 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 162.158.111.0/24
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
⚙️ General Security
Add 162.158.111.124 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
Neighbors in 162.158.111.0/24
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
162.158.111.124 has been assigned a threat score of 140/100 (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 162.158.111.124 originates from Frankfurt, Germany, operating on the network of Cloudflare, Inc.. It was identified through automated analysis of incoming network traffic across monitored endpoints. Over a period of 16 days, this IP generated 243 malicious requests, averaging approximately 15.2 requests per day. Germany currently accounts for 121 blocked IPs in our database, making it a significant source of malicious traffic. With a threat score of 140/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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💡 Responsible Disclosure Ethics
Responsible disclosure balances public safety with giving vendors time to patch vulnerabilities. The security community generally supports coordinated disclosure timelines, but disagreements about appropriate timeframes and full disclosure continue to drive policy debates.
💡 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.