
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Directory Scan | Behavioral anomaly detected by automated analysis | +0 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Block 195.210.114.69 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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.
195.210.114.69 has been assigned a threat score of 100/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
Network traffic from 195.210.114.69, located in Boise, United States, operating on the network of GSL Networks Pty LTD, has been classified as malicious by our automated threat scoring engine. The address has been active for 13 days in our monitoring system, producing 376 flagged requests at a rate of ~28.9/day. United States currently accounts for 143 blocked IPs in our database, making it a significant source of malicious traffic. With a threat score of 100/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
Monitoring DNS queries reveals malicious activity including command-and-control communication, data exfiltration through DNS tunneling, and connections to known malicious domains. DNS is often the first indicator of compromise in network forensics.
WAFs inspect HTTP traffic to block common attacks but require careful tuning. Overly aggressive rules cause false positives while permissive configurations miss attacks. Modern WAFs combine signature matching with behavioral analysis and machine learning.