
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 94.156.205.79 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.
94.156.205.79 has been assigned a threat score of 95/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
The address 94.156.205.79 originates from Taipei, Taiwan, operating on the network of PacketHub S.A.. It was identified through automated analysis of incoming network traffic across monitored endpoints. During its 16-day observation window, we recorded 403 hostile requests from this IP — roughly 25.2 per day on average. Taiwan currently accounts for 101 blocked IPs in our database, making it a significant source of malicious traffic. A score of 95/100 places this address in the top tier of severity. Block and investigate any historical connections.
Nation-state actors conduct sophisticated campaigns for espionage, sabotage, and influence operations. Their resources exceed typical criminal organizations, enabling zero-day exploitation, long-term persistent access, and attacks on critical infrastructure.
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.